Tutorial for Mesoscale Two-Photon Calcium Imaging of Population Level Odor Responses from the Mouse Olfactory Bulb.¶

This tutorial shows how to access the data related to DANDI:001170 using pynwb.

This dataset contains the 2-photon calcium imaging, treadmill position and velocity traces and odor stimulus events, along with subject and session metadata.

Contents:

• Streaming an NWB file
• Reading an NWB file
• Access Subject and Session metadata
• Access Raw Imaging
• Access Processed Imaging
• Access Odor Stimuli
• Access Treadmill traces

Select the subject and session you want to load¶

In [1]:

subject_id = 134
session_id = 22
nwbfile_path = f"sub-{subject_id}/sub-{subject_id}_ses-{session_id}_behavior+ophys.nwb"

subject_id = 134 session_id = 22 nwbfile_path = f"sub-{subject_id}/sub-{subject_id}_ses-{session_id}_behavior+ophys.nwb"

Streaming an NWB file ¶

This section demonstrates how to access the files on the DANDI Archive without downloading them. Based on the Streaming NWB files tutorial from PyNWB.

The dandi.dandiapi.DandiAPIClient can be used to get the S3 URL of the NWB file stored in the DANDI Archive.

In [2]:

from dandi.dandiapi import DandiAPIClient

dandiset_id = "001170"

with DandiAPIClient() as client:
    asset = client.get_dandiset(dandiset_id, 'draft').get_asset_by_path(nwbfile_path)
    s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)

from dandi.dandiapi import DandiAPIClient dandiset_id = "001170" with DandiAPIClient() as client: asset = client.get_dandiset(dandiset_id, 'draft').get_asset_by_path(nwbfile_path) s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)

We will use remfile for streaming the file. You can read more about remfile at this tutorial section.

In [3]:

import h5py
import remfile

file = remfile.File(s3_url)
h5_file = h5py.File(file, "r")

import h5py import remfile file = remfile.File(s3_url) h5_file = h5py.File(file, "r")

Reading an NWB file ¶

This section demonstrates how to read an NWB file using pynwb.

Based on the NWB File Basics tutorial from PyNWB.

An NWBFile represents a single session of an experiment. Each NWBFile must have a session description, identifier, and session start time.

Reading is carried out using the NWBHDF5IO class. To read the NWB file use the read mode ("r") to retrieve an NWBFile object.

In [4]:

from pynwb import NWBHDF5IO
io = NWBHDF5IO(file=h5_file, load_namespaces=True)
nwbfile = io.read()
nwbfile

from pynwb import NWBHDF5IO io = NWBHDF5IO(file=h5_file, load_namespaces=True) nwbfile = io.read() nwbfile

Out[4]:

root (NWBFile)

session_description: In this study, we explore odor-evoked activity representation in the olfactory bulb (OB) and how odor responses enable odor discrimination. Contrary to some previously cited theories that suggest a sparse representation, we hypothesize a more dense representation during odor presentation. A key question is how odors are reliably encoded in OB activity patterns, and how these patterns contribute to early odor processing. To address this problem, we recorded population level odor responses from the mouse OB with mesoscale two photon calcium imaging and applied machine learning techniques to suggest a model in which sparse coding is largely sufficient for olfaction, but redundant information may make odor coding more robust across different variables.

identifier: ed03639d-3530-41f0-ab25-a0c31631a85a

session_start_time

2022-07-21 00:00:00-05:00

timestamps_reference_time

2022-07-21 00:00:00-05:00

file_create_date

0

2024-08-07 00:21:45.469158+02:00

experimenter

('Pirhayatifard, Delaram', 'Hanson, Elizabeth', 'Pfaffinger, Paul', 'Arenkiel, Benjamin', 'Reimer, Jacob')

related_publications

('https://doi.org/10.1101/2023.04.24.538157', 'https://icml-compbio.github.io/2023/papers/WCBICML2023_paper122.pdf')

acquisition

treadmill_position

resolution: -1.0

comments: no comments

description: treadmill position from Treadmill table

conversion: 1.0

offset: 0.0

unit: meters

data

timestamps

timestamps_unit: seconds

interval: 1

reference_frame: unknown

timestamp_link

0: processing/behavior/treadmill_velocity/timestamps

two_photon_series_FOV1_channel1

resolution: -1.0

comments: no comments

description: FOV1 refers to the left part of the original ScanImage acquisition. To maximize frame rates in each imaging session, fields of view were defined for acquisition that included a single plane visualizing only the dorsal surface of bilateral OBs (1800 um long x ~600um wide fields of view that were tiled to cover a total field of view that was 1800-2500 um wide). Images were acquired continuously throughout experiments (during odor presentations, intertrial, and inter-block intervals), with 5um/pixel resolution at the fastest possible frame rate allowed by the imaging parameters (15-18 Hz).

conversion: 1.0

offset: 0.0

unit: n.a.

data

timestamps

timestamps_unit: seconds

interval: 1

dimension

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

two_photon_series_FOV2_channel1

resolution: -1.0

comments: no comments

description: FOV2 refers to the central part of the original ScanImage acquisition. To maximize frame rates in each imaging session, fields of view were defined for acquisition that included a single plane visualizing only the dorsal surface of bilateral OBs (1800 um long x ~600um wide fields of view that were tiled to cover a total field of view that was 1800-2500 um wide). Images were acquired continuously throughout experiments (during odor presentations, intertrial, and inter-block intervals), with 5um/pixel resolution at the fastest possible frame rate allowed by the imaging parameters (15-18 Hz).

conversion: 1.0

offset: 0.0

unit: n.a.

data

timestamps

timestamps_unit: seconds

interval: 1

dimension

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

two_photon_series_FOV3_channel1

resolution: -1.0

comments: no comments

description: FOV3 refers to the right part of the original ScanImage acquisition. To maximize frame rates in each imaging session, fields of view were defined for acquisition that included a single plane visualizing only the dorsal surface of bilateral OBs (1800 um long x ~600um wide fields of view that were tiled to cover a total field of view that was 1800-2500 um wide). Images were acquired continuously throughout experiments (during odor presentations, intertrial, and inter-block intervals), with 5um/pixel resolution at the fastest possible frame rate allowed by the imaging parameters (15-18 Hz).

conversion: 1.0

offset: 0.0

unit: n.a.

data

timestamps

timestamps_unit: seconds

interval: 1

dimension

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

keywords

processing

behavior

description: behavioral data processing

data_interfaces

treadmill_velocity

resolution: -1.0

comments: no comments

description: treadmill velocity from Treadmill table

conversion: 1.0

offset: 0.0

unit: unknown

data

timestamps (link to acquisition/treadmill_position/timestamps)

timestamps_unit: seconds

interval: 1

ophys

description: ophys data processing

data_interfaces

average_images

description: Average image of from SummaryImages.Average table.

images

average_image_FOV1_channel1

average_image_FOV2_channel1

average_image_FOV3_channel1

correlation_images

description: Correlation image from SummaryImages.Correlation table.

images

correlation_image_FOV1_channel1

correlation_image_FOV2_channel1

correlation_image_FOV3_channel1

fluorescence

roi_response_series

fluorescence_FOV1_channel1

resolution: -1.0

comments: no comments

description: Fluorescence traces from FOV1 Channel1

conversion: 1.0

offset: 0.0

unit: n.a.

data

timestamps

timestamps_unit: seconds

interval: 1

rois

description: all ROIs

table

description: Output from segmenting FOV1 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[150, 83, 1.0], [151, 83, 1.0], [152, 83, 1.0], [153, 83, 1.0], [154, 83, 1.0], [155, 83, 1.0], [156, 83, 1.0], [157, 83, 1.0], [158, 83, 1.0], [159, 83, 1.0], [160, 83, 1.0], [161, 83, 1.0], [149, 84, 1.0], [150, 84, 1.0], [151, 84, 1.0], [152, 84, 1.0], [153, 84, 1.0], [154, 84, 1.0], [155, 84, 1.0], [156, 84, 1.0], [157, 84, 1.0], [158, 84, 1.0], [159, 84, 1.0], [160, 84, 1.0], [161, 84, 1.0], [162, 84, 1.0], [163, 84, 1.0], [147, 85, 1.0], [148, 85, 1.0], [149, 85, 1.0], [150, 85, 1.0], [151, 85, 1.0], [152, 85, 1.0], [153, 85, 1.0], [154, 85, 1.0], [155, 85, 1.0], [156, 85, 1.0], [157, 85, 1.0], [158, 85, 1.0], [159, 85, 1.0], [160, 85, 1.0], [161, 85, 1.0], [162, 85, 1.0], [163, 85, 1.0], [164, 85, 1.0], [147, 86, 1.0], [148, 86, 1.0], [149, 86, 1.0], [150, 86, 1.0], [151, 86, 1.0], [152, 86, 1.0], [153, 86, 1.0], [154, 86, 1.0], [155, 86, 1.0], [156, 86, 1.0], [157, 86, 1.0], [158, 86, 1.0], [159, 86, 1.0], [160, 86, 1.0], [161, 86, 1.0], [162, 86, 1.0], [163, 86, 1.0], [164, 86, 1.0], [146, 87, 1.0], [147, 87, 1.0], [148, 87, 1.0], [149, 87, 1.0], [150, 87, 1.0], [151, 87, 1.0], [152, 87, 1.0], [153, 87, 1.0], [154, 87, 1.0], [155, 87, 1.0], [156, 87, 1.0], [157, 87, 1.0], [158, 87, 1.0], [159, 87, 1.0], [160, 87, 1.0], [161, 87, 1.0], [162, 87, 1.0], [163, 87, 1.0], [164, 87, 1.0], [147, 88, 1.0], [148, 88, 1.0], [149, 88, 1.0], [150, 88, 1.0], [151, 88, 1.0], [152, 88, 1.0], [153, 88, 1.0], [154, 88, 1.0], [155, 88, 1.0], [156, 88, 1.0], [157, 88, 1.0], [158, 88, 1.0], [159, 88, 1.0], [160, 88, 1.0], [161, 88, 1.0], [162, 88, 1.0], [163, 88, 1.0], [148, 89, 1.0], ...]
1	[[152, 91, 1.0], [153, 91, 1.0], [154, 91, 1.0], [155, 91, 1.0], [156, 91, 1.0], [150, 92, 1.0], [151, 92, 1.0], [152, 92, 1.0], [153, 92, 1.0], [154, 92, 1.0], [155, 92, 1.0], [156, 92, 1.0], [157, 92, 1.0], [149, 93, 1.0], [150, 93, 1.0], [151, 93, 1.0], [152, 93, 1.0], [153, 93, 1.0], [154, 93, 1.0], [155, 93, 1.0], [156, 93, 1.0], [157, 93, 1.0], [158, 93, 1.0], [148, 94, 1.0], [149, 94, 1.0], [150, 94, 1.0], [151, 94, 1.0], [152, 94, 1.0], [153, 94, 1.0], [154, 94, 1.0], [155, 94, 1.0], [156, 94, 1.0], [157, 94, 1.0], [158, 94, 1.0], [159, 94, 1.0], [148, 95, 1.0], [149, 95, 1.0], [150, 95, 1.0], [151, 95, 1.0], [152, 95, 1.0], [153, 95, 1.0], [154, 95, 1.0], [155, 95, 1.0], [156, 95, 1.0], [157, 95, 1.0], [158, 95, 1.0], [159, 95, 1.0], [160, 95, 1.0], [148, 96, 1.0], [149, 96, 1.0], [150, 96, 1.0], [151, 96, 1.0], [152, 96, 1.0], [153, 96, 1.0], [154, 96, 1.0], [155, 96, 1.0], [156, 96, 1.0], [157, 96, 1.0], [158, 96, 1.0], [159, 96, 1.0], [160, 96, 1.0], [147, 97, 1.0], [148, 97, 1.0], [149, 97, 1.0], [150, 97, 1.0], [151, 97, 1.0], [152, 97, 1.0], [153, 97, 1.0], [154, 97, 1.0], [155, 97, 1.0], [156, 97, 1.0], [157, 97, 1.0], [158, 97, 1.0], [159, 97, 1.0], [160, 97, 1.0], [161, 97, 1.0], [147, 98, 1.0], [148, 98, 1.0], [149, 98, 1.0], [150, 98, 1.0], [151, 98, 1.0], [152, 98, 1.0], [153, 98, 1.0], [154, 98, 1.0], [155, 98, 1.0], [156, 98, 1.0], [157, 98, 1.0], [158, 98, 1.0], [159, 98, 1.0], [160, 98, 1.0], [161, 98, 1.0], [148, 99, 1.0], [149, 99, 1.0], [150, 99, 1.0], [151, 99, 1.0], [152, 99, 1.0], [153, 99, 1.0], [154, 99, 1.0], [155, 99, 1.0], [156, 99, 1.0], ...]
2	[[145, 100, 1.0], [146, 100, 1.0], [147, 100, 1.0], [143, 101, 1.0], [144, 101, 1.0], [145, 101, 1.0], [146, 101, 1.0], [147, 101, 1.0], [148, 101, 1.0], [149, 101, 1.0], [142, 102, 1.0], [143, 102, 1.0], [144, 102, 1.0], [145, 102, 1.0], [146, 102, 1.0], [147, 102, 1.0], [148, 102, 1.0], [149, 102, 1.0], [150, 102, 1.0], [141, 103, 1.0], [142, 103, 1.0], [143, 103, 1.0], [144, 103, 1.0], [145, 103, 1.0], [146, 103, 1.0], [147, 103, 1.0], [148, 103, 1.0], [149, 103, 1.0], [150, 103, 1.0], [151, 103, 1.0], [140, 104, 1.0], [141, 104, 1.0], [142, 104, 1.0], [143, 104, 1.0], [144, 104, 1.0], [145, 104, 1.0], [146, 104, 1.0], [147, 104, 1.0], [148, 104, 1.0], [149, 104, 1.0], [150, 104, 1.0], [151, 104, 1.0], [152, 104, 1.0], [140, 105, 1.0], [141, 105, 1.0], [142, 105, 1.0], [143, 105, 1.0], [144, 105, 1.0], [145, 105, 1.0], [146, 105, 1.0], [147, 105, 1.0], [148, 105, 1.0], [149, 105, 1.0], [150, 105, 1.0], [151, 105, 1.0], [152, 105, 1.0], [139, 106, 1.0], [140, 106, 1.0], [141, 106, 1.0], [142, 106, 1.0], [143, 106, 1.0], [144, 106, 1.0], [145, 106, 1.0], [146, 106, 1.0], [147, 106, 1.0], [148, 106, 1.0], [149, 106, 1.0], [150, 106, 1.0], [151, 106, 1.0], [152, 106, 1.0], [139, 107, 1.0], [140, 107, 1.0], [141, 107, 1.0], [142, 107, 1.0], [143, 107, 1.0], [144, 107, 1.0], [145, 107, 1.0], [146, 107, 1.0], [147, 107, 1.0], [148, 107, 1.0], [149, 107, 1.0], [150, 107, 1.0], [151, 107, 1.0], [152, 107, 1.0], [139, 108, 1.0], [140, 108, 1.0], [141, 108, 1.0], [142, 108, 1.0], [143, 108, 1.0], [144, 108, 1.0], [145, 108, 1.0], [146, 108, 1.0], [147, 108, 1.0], [148, 108, 1.0], [149, 108, 1.0], [150, 108, 1.0], [151, 108, 1.0], [152, 108, 1.0], [140, 109, 1.0], [141, 109, 1.0], ...]
3	[[138, 90, 1.0], [139, 90, 1.0], [140, 90, 1.0], [137, 91, 1.0], [138, 91, 1.0], [139, 91, 1.0], [140, 91, 1.0], [141, 91, 1.0], [136, 92, 1.0], [137, 92, 1.0], [138, 92, 1.0], [139, 92, 1.0], [140, 92, 1.0], [141, 92, 1.0], [142, 92, 1.0], [143, 92, 1.0], [135, 93, 1.0], [136, 93, 1.0], [137, 93, 1.0], [138, 93, 1.0], [139, 93, 1.0], [140, 93, 1.0], [141, 93, 1.0], [142, 93, 1.0], [143, 93, 1.0], [144, 93, 1.0], [134, 94, 1.0], [135, 94, 1.0], [136, 94, 1.0], [137, 94, 1.0], [138, 94, 1.0], [139, 94, 1.0], [140, 94, 1.0], [141, 94, 1.0], [142, 94, 1.0], [143, 94, 1.0], [144, 94, 1.0], [134, 95, 1.0], [135, 95, 1.0], [136, 95, 1.0], [137, 95, 1.0], [138, 95, 1.0], [139, 95, 1.0], [140, 95, 1.0], [141, 95, 1.0], [142, 95, 1.0], [143, 95, 1.0], [144, 95, 1.0], [145, 95, 1.0], [134, 96, 1.0], [135, 96, 1.0], [136, 96, 1.0], [137, 96, 1.0], [138, 96, 1.0], [139, 96, 1.0], [140, 96, 1.0], [141, 96, 1.0], [142, 96, 1.0], [143, 96, 1.0], [144, 96, 1.0], [135, 97, 1.0], [136, 97, 1.0], [137, 97, 1.0], [138, 97, 1.0], [139, 97, 1.0], [140, 97, 1.0], [141, 97, 1.0], [142, 97, 1.0], [143, 97, 1.0], [144, 97, 1.0], [135, 98, 1.0], [136, 98, 1.0], [137, 98, 1.0], [138, 98, 1.0], [139, 98, 1.0], [140, 98, 1.0], [141, 98, 1.0], [142, 98, 1.0], [143, 98, 1.0], [144, 98, 1.0], [136, 99, 1.0], [137, 99, 1.0], [138, 99, 1.0], [139, 99, 1.0], [140, 99, 1.0], [141, 99, 1.0], [142, 99, 1.0], [143, 99, 1.0], [137, 100, 1.0], [138, 100, 1.0], [139, 100, 1.0], [140, 100, 1.0], [141, 100, 1.0], [142, 100, 1.0], [138, 101, 1.0], [139, 101, 1.0], [140, 101, 1.0]]

... and 13 more rows.

fluorescence_FOV2_channel1

resolution: -1.0

comments: no comments

description: Fluorescence traces from FOV2 Channel1

conversion: 1.0

offset: 0.0

unit: n.a.

data

timestamps

timestamps_unit: seconds

interval: 1

rois

description: all ROIs

table

description: Output from segmenting FOV2 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[159, 51, 1.0], [160, 51, 1.0], [154, 52, 1.0], [155, 52, 1.0], [156, 52, 1.0], [157, 52, 1.0], [158, 52, 1.0], [159, 52, 1.0], [160, 52, 1.0], [161, 52, 1.0], [162, 52, 1.0], [151, 53, 1.0], [152, 53, 1.0], [153, 53, 1.0], [154, 53, 1.0], [155, 53, 1.0], [156, 53, 1.0], [157, 53, 1.0], [158, 53, 1.0], [159, 53, 1.0], [160, 53, 1.0], [161, 53, 1.0], [162, 53, 1.0], [163, 53, 1.0], [150, 54, 1.0], [151, 54, 1.0], [152, 54, 1.0], [153, 54, 1.0], [154, 54, 1.0], [155, 54, 1.0], [156, 54, 1.0], [157, 54, 1.0], [158, 54, 1.0], [159, 54, 1.0], [160, 54, 1.0], [161, 54, 1.0], [162, 54, 1.0], [163, 54, 1.0], [149, 55, 1.0], [150, 55, 1.0], [151, 55, 1.0], [152, 55, 1.0], [153, 55, 1.0], [154, 55, 1.0], [155, 55, 1.0], [156, 55, 1.0], [157, 55, 1.0], [158, 55, 1.0], [159, 55, 1.0], [160, 55, 1.0], [161, 55, 1.0], [162, 55, 1.0], [163, 55, 1.0], [164, 55, 1.0], [149, 56, 1.0], [150, 56, 1.0], [151, 56, 1.0], [152, 56, 1.0], [153, 56, 1.0], [154, 56, 1.0], [155, 56, 1.0], [156, 56, 1.0], [157, 56, 1.0], [158, 56, 1.0], [159, 56, 1.0], [160, 56, 1.0], [161, 56, 1.0], [162, 56, 1.0], [163, 56, 1.0], [164, 56, 1.0], [148, 57, 1.0], [149, 57, 1.0], [150, 57, 1.0], [151, 57, 1.0], [152, 57, 1.0], [153, 57, 1.0], [154, 57, 1.0], [155, 57, 1.0], [156, 57, 1.0], [157, 57, 1.0], [158, 57, 1.0], [159, 57, 1.0], [160, 57, 1.0], [161, 57, 1.0], [162, 57, 1.0], [163, 57, 1.0], [164, 57, 1.0], [165, 57, 1.0], [148, 58, 1.0], [149, 58, 1.0], [150, 58, 1.0], [151, 58, 1.0], [152, 58, 1.0], [153, 58, 1.0], [154, 58, 1.0], [155, 58, 1.0], [156, 58, 1.0], [157, 58, 1.0], [158, 58, 1.0], [159, 58, 1.0], ...]
1	[[136, 63, 1.0], [137, 63, 1.0], [138, 63, 1.0], [139, 63, 1.0], [140, 63, 1.0], [141, 63, 1.0], [135, 64, 1.0], [136, 64, 1.0], [137, 64, 1.0], [138, 64, 1.0], [139, 64, 1.0], [140, 64, 1.0], [141, 64, 1.0], [142, 64, 1.0], [143, 64, 1.0], [134, 65, 1.0], [135, 65, 1.0], [136, 65, 1.0], [137, 65, 1.0], [138, 65, 1.0], [139, 65, 1.0], [140, 65, 1.0], [141, 65, 1.0], [142, 65, 1.0], [143, 65, 1.0], [133, 66, 1.0], [134, 66, 1.0], [135, 66, 1.0], [136, 66, 1.0], [137, 66, 1.0], [138, 66, 1.0], [139, 66, 1.0], [140, 66, 1.0], [141, 66, 1.0], [142, 66, 1.0], [143, 66, 1.0], [144, 66, 1.0], [133, 67, 1.0], [134, 67, 1.0], [135, 67, 1.0], [136, 67, 1.0], [137, 67, 1.0], [138, 67, 1.0], [139, 67, 1.0], [140, 67, 1.0], [141, 67, 1.0], [142, 67, 1.0], [143, 67, 1.0], [144, 67, 1.0], [145, 67, 1.0], [133, 68, 1.0], [134, 68, 1.0], [135, 68, 1.0], [136, 68, 1.0], [137, 68, 1.0], [138, 68, 1.0], [139, 68, 1.0], [140, 68, 1.0], [141, 68, 1.0], [142, 68, 1.0], [143, 68, 1.0], [144, 68, 1.0], [145, 68, 1.0], [132, 69, 1.0], [133, 69, 1.0], [134, 69, 1.0], [135, 69, 1.0], [136, 69, 1.0], [137, 69, 1.0], [138, 69, 1.0], [139, 69, 1.0], [140, 69, 1.0], [141, 69, 1.0], [142, 69, 1.0], [143, 69, 1.0], [144, 69, 1.0], [145, 69, 1.0], [146, 69, 1.0], [132, 70, 1.0], [133, 70, 1.0], [134, 70, 1.0], [135, 70, 1.0], [136, 70, 1.0], [137, 70, 1.0], [138, 70, 1.0], [139, 70, 1.0], [140, 70, 1.0], [141, 70, 1.0], [142, 70, 1.0], [143, 70, 1.0], [144, 70, 1.0], [145, 70, 1.0], [146, 70, 1.0], [147, 70, 1.0], [132, 71, 1.0], [133, 71, 1.0], [134, 71, 1.0], [135, 71, 1.0], [136, 71, 1.0], [137, 71, 1.0], ...]
2	[[144, 96, 1.0], [145, 96, 1.0], [150, 96, 1.0], [151, 96, 1.0], [142, 97, 1.0], [143, 97, 1.0], [144, 97, 1.0], [145, 97, 1.0], [146, 97, 1.0], [147, 97, 1.0], [148, 97, 1.0], [149, 97, 1.0], [150, 97, 1.0], [151, 97, 1.0], [152, 97, 1.0], [140, 98, 1.0], [141, 98, 1.0], [142, 98, 1.0], [143, 98, 1.0], [144, 98, 1.0], [145, 98, 1.0], [146, 98, 1.0], [147, 98, 1.0], [148, 98, 1.0], [149, 98, 1.0], [150, 98, 1.0], [151, 98, 1.0], [152, 98, 1.0], [153, 98, 1.0], [139, 99, 1.0], [140, 99, 1.0], [141, 99, 1.0], [142, 99, 1.0], [143, 99, 1.0], [144, 99, 1.0], [145, 99, 1.0], [146, 99, 1.0], [147, 99, 1.0], [148, 99, 1.0], [149, 99, 1.0], [150, 99, 1.0], [151, 99, 1.0], [152, 99, 1.0], [153, 99, 1.0], [139, 100, 1.0], [140, 100, 1.0], [141, 100, 1.0], [142, 100, 1.0], [143, 100, 1.0], [144, 100, 1.0], [145, 100, 1.0], [146, 100, 1.0], [147, 100, 1.0], [148, 100, 1.0], [149, 100, 1.0], [150, 100, 1.0], [151, 100, 1.0], [152, 100, 1.0], [153, 100, 1.0], [139, 101, 1.0], [140, 101, 1.0], [141, 101, 1.0], [142, 101, 1.0], [143, 101, 1.0], [144, 101, 1.0], [145, 101, 1.0], [146, 101, 1.0], [147, 101, 1.0], [148, 101, 1.0], [149, 101, 1.0], [150, 101, 1.0], [151, 101, 1.0], [152, 101, 1.0], [153, 101, 1.0], [140, 102, 1.0], [141, 102, 1.0], [142, 102, 1.0], [143, 102, 1.0], [144, 102, 1.0], [145, 102, 1.0], [146, 102, 1.0], [147, 102, 1.0], [148, 102, 1.0], [149, 102, 1.0], [150, 102, 1.0], [151, 102, 1.0], [152, 102, 1.0], [142, 103, 1.0], [143, 103, 1.0], [144, 103, 1.0], [145, 103, 1.0], [146, 103, 1.0], [147, 103, 1.0], [148, 103, 1.0], [149, 103, 1.0], [150, 103, 1.0], [151, 103, 1.0], [143, 104, 1.0], [144, 104, 1.0], [145, 104, 1.0], ...]
3	[[146, 85, 1.0], [144, 86, 1.0], [145, 86, 1.0], [146, 86, 1.0], [147, 86, 1.0], [148, 86, 1.0], [149, 86, 1.0], [143, 87, 1.0], [144, 87, 1.0], [145, 87, 1.0], [146, 87, 1.0], [147, 87, 1.0], [148, 87, 1.0], [149, 87, 1.0], [150, 87, 1.0], [142, 88, 1.0], [143, 88, 1.0], [144, 88, 1.0], [145, 88, 1.0], [146, 88, 1.0], [147, 88, 1.0], [148, 88, 1.0], [149, 88, 1.0], [150, 88, 1.0], [142, 89, 1.0], [143, 89, 1.0], [144, 89, 1.0], [145, 89, 1.0], [146, 89, 1.0], [147, 89, 1.0], [148, 89, 1.0], [149, 89, 1.0], [150, 89, 1.0], [151, 89, 1.0], [141, 90, 1.0], [142, 90, 1.0], [143, 90, 1.0], [144, 90, 1.0], [145, 90, 1.0], [146, 90, 1.0], [147, 90, 1.0], [148, 90, 1.0], [149, 90, 1.0], [150, 90, 1.0], [151, 90, 1.0], [152, 90, 1.0], [141, 91, 1.0], [142, 91, 1.0], [143, 91, 1.0], [144, 91, 1.0], [145, 91, 1.0], [146, 91, 1.0], [147, 91, 1.0], [148, 91, 1.0], [149, 91, 1.0], [150, 91, 1.0], [151, 91, 1.0], [152, 91, 1.0], [142, 92, 1.0], [143, 92, 1.0], [144, 92, 1.0], [145, 92, 1.0], [146, 92, 1.0], [147, 92, 1.0], [148, 92, 1.0], [149, 92, 1.0], [150, 92, 1.0], [151, 92, 1.0], [152, 92, 1.0], [142, 93, 1.0], [143, 93, 1.0], [144, 93, 1.0], [145, 93, 1.0], [146, 93, 1.0], [147, 93, 1.0], [148, 93, 1.0], [149, 93, 1.0], [150, 93, 1.0], [151, 93, 1.0], [152, 93, 1.0], [143, 94, 1.0], [144, 94, 1.0], [145, 94, 1.0], [146, 94, 1.0], [147, 94, 1.0], [148, 94, 1.0], [149, 94, 1.0], [150, 94, 1.0], [151, 94, 1.0], [144, 95, 1.0], [145, 95, 1.0], [146, 95, 1.0], [147, 95, 1.0], [148, 95, 1.0], [149, 95, 1.0], [150, 95, 1.0], [151, 95, 1.0], [146, 96, 1.0], [147, 96, 1.0], [148, 96, 1.0], ...]

... and 44 more rows.

fluorescence_FOV3_channel1

resolution: -1.0

comments: no comments

description: Fluorescence traces from FOV3 Channel1

conversion: 1.0

offset: 0.0

unit: n.a.

data

timestamps

timestamps_unit: seconds

interval: 1

rois

description: all ROIs

table

description: Output from segmenting FOV3 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[335, 89, 1.0], [332, 90, 1.0], [333, 90, 1.0], [334, 90, 1.0], [335, 90, 1.0], [336, 90, 1.0], [337, 90, 1.0], [338, 90, 1.0], [339, 90, 1.0], [327, 91, 1.0], [328, 91, 1.0], [329, 91, 1.0], [330, 91, 1.0], [331, 91, 1.0], [332, 91, 1.0], [333, 91, 1.0], [334, 91, 1.0], [335, 91, 1.0], [336, 91, 1.0], [337, 91, 1.0], [338, 91, 1.0], [339, 91, 1.0], [340, 91, 1.0], [326, 92, 1.0], [327, 92, 1.0], [328, 92, 1.0], [329, 92, 1.0], [330, 92, 1.0], [331, 92, 1.0], [332, 92, 1.0], [333, 92, 1.0], [334, 92, 1.0], [335, 92, 1.0], [336, 92, 1.0], [337, 92, 1.0], [338, 92, 1.0], [339, 92, 1.0], [340, 92, 1.0], [341, 92, 1.0], [324, 93, 1.0], [325, 93, 1.0], [326, 93, 1.0], [327, 93, 1.0], [328, 93, 1.0], [329, 93, 1.0], [330, 93, 1.0], [331, 93, 1.0], [332, 93, 1.0], [333, 93, 1.0], [334, 93, 1.0], [335, 93, 1.0], [336, 93, 1.0], [337, 93, 1.0], [338, 93, 1.0], [339, 93, 1.0], [340, 93, 1.0], [341, 93, 1.0], [342, 93, 1.0], [323, 94, 1.0], [324, 94, 1.0], [325, 94, 1.0], [326, 94, 1.0], [327, 94, 1.0], [328, 94, 1.0], [329, 94, 1.0], [330, 94, 1.0], [331, 94, 1.0], [332, 94, 1.0], [333, 94, 1.0], [334, 94, 1.0], [335, 94, 1.0], [336, 94, 1.0], [337, 94, 1.0], [338, 94, 1.0], [339, 94, 1.0], [340, 94, 1.0], [341, 94, 1.0], [342, 94, 1.0], [322, 95, 1.0], [323, 95, 1.0], [324, 95, 1.0], [325, 95, 1.0], [326, 95, 1.0], [327, 95, 1.0], [328, 95, 1.0], [329, 95, 1.0], [330, 95, 1.0], [331, 95, 1.0], [332, 95, 1.0], [333, 95, 1.0], [334, 95, 1.0], [335, 95, 1.0], [336, 95, 1.0], [337, 95, 1.0], [338, 95, 1.0], [339, 95, 1.0], [340, 95, 1.0], [341, 95, 1.0], [342, 95, 1.0], [343, 95, 1.0], ...]
1	[[325, 77, 1.0], [326, 77, 1.0], [327, 77, 1.0], [328, 77, 1.0], [329, 77, 1.0], [323, 78, 1.0], [324, 78, 1.0], [325, 78, 1.0], [326, 78, 1.0], [327, 78, 1.0], [328, 78, 1.0], [329, 78, 1.0], [330, 78, 1.0], [321, 79, 1.0], [322, 79, 1.0], [323, 79, 1.0], [324, 79, 1.0], [325, 79, 1.0], [326, 79, 1.0], [327, 79, 1.0], [328, 79, 1.0], [329, 79, 1.0], [330, 79, 1.0], [331, 79, 1.0], [320, 80, 1.0], [321, 80, 1.0], [322, 80, 1.0], [323, 80, 1.0], [324, 80, 1.0], [325, 80, 1.0], [326, 80, 1.0], [327, 80, 1.0], [328, 80, 1.0], [329, 80, 1.0], [330, 80, 1.0], [331, 80, 1.0], [332, 80, 1.0], [319, 81, 1.0], [320, 81, 1.0], [321, 81, 1.0], [322, 81, 1.0], [323, 81, 1.0], [324, 81, 1.0], [325, 81, 1.0], [326, 81, 1.0], [327, 81, 1.0], [328, 81, 1.0], [329, 81, 1.0], [330, 81, 1.0], [331, 81, 1.0], [332, 81, 1.0], [319, 82, 1.0], [320, 82, 1.0], [321, 82, 1.0], [322, 82, 1.0], [323, 82, 1.0], [324, 82, 1.0], [325, 82, 1.0], [326, 82, 1.0], [327, 82, 1.0], [328, 82, 1.0], [329, 82, 1.0], [330, 82, 1.0], [331, 82, 1.0], [332, 82, 1.0], [333, 82, 1.0], [318, 83, 1.0], [319, 83, 1.0], [320, 83, 1.0], [321, 83, 1.0], [322, 83, 1.0], [323, 83, 1.0], [324, 83, 1.0], [325, 83, 1.0], [326, 83, 1.0], [327, 83, 1.0], [328, 83, 1.0], [329, 83, 1.0], [330, 83, 1.0], [331, 83, 1.0], [332, 83, 1.0], [333, 83, 1.0], [334, 83, 1.0], [318, 84, 1.0], [319, 84, 1.0], [320, 84, 1.0], [321, 84, 1.0], [322, 84, 1.0], [323, 84, 1.0], [324, 84, 1.0], [325, 84, 1.0], [326, 84, 1.0], [327, 84, 1.0], [328, 84, 1.0], [329, 84, 1.0], [330, 84, 1.0], [331, 84, 1.0], [332, 84, 1.0], [333, 84, 1.0], [334, 84, 1.0], ...]
2	[[351, 92, 1.0], [352, 92, 1.0], [353, 92, 1.0], [354, 92, 1.0], [355, 92, 1.0], [349, 93, 1.0], [350, 93, 1.0], [351, 93, 1.0], [352, 93, 1.0], [353, 93, 1.0], [354, 93, 1.0], [355, 93, 1.0], [356, 93, 1.0], [357, 93, 1.0], [347, 94, 1.0], [348, 94, 1.0], [349, 94, 1.0], [350, 94, 1.0], [351, 94, 1.0], [352, 94, 1.0], [353, 94, 1.0], [354, 94, 1.0], [355, 94, 1.0], [356, 94, 1.0], [357, 94, 1.0], [358, 94, 1.0], [347, 95, 1.0], [348, 95, 1.0], [349, 95, 1.0], [350, 95, 1.0], [351, 95, 1.0], [352, 95, 1.0], [353, 95, 1.0], [354, 95, 1.0], [355, 95, 1.0], [356, 95, 1.0], [357, 95, 1.0], [358, 95, 1.0], [359, 95, 1.0], [346, 96, 1.0], [347, 96, 1.0], [348, 96, 1.0], [349, 96, 1.0], [350, 96, 1.0], [351, 96, 1.0], [352, 96, 1.0], [353, 96, 1.0], [354, 96, 1.0], [355, 96, 1.0], [356, 96, 1.0], [357, 96, 1.0], [358, 96, 1.0], [359, 96, 1.0], [360, 96, 1.0], [345, 97, 1.0], [346, 97, 1.0], [347, 97, 1.0], [348, 97, 1.0], [349, 97, 1.0], [350, 97, 1.0], [351, 97, 1.0], [352, 97, 1.0], [353, 97, 1.0], [354, 97, 1.0], [355, 97, 1.0], [356, 97, 1.0], [357, 97, 1.0], [358, 97, 1.0], [359, 97, 1.0], [360, 97, 1.0], [361, 97, 1.0], [344, 98, 1.0], [345, 98, 1.0], [346, 98, 1.0], [347, 98, 1.0], [348, 98, 1.0], [349, 98, 1.0], [350, 98, 1.0], [351, 98, 1.0], [352, 98, 1.0], [353, 98, 1.0], [354, 98, 1.0], [355, 98, 1.0], [356, 98, 1.0], [357, 98, 1.0], [358, 98, 1.0], [359, 98, 1.0], [360, 98, 1.0], [361, 98, 1.0], [362, 98, 1.0], [344, 99, 1.0], [345, 99, 1.0], [346, 99, 1.0], [347, 99, 1.0], [348, 99, 1.0], [349, 99, 1.0], [350, 99, 1.0], [351, 99, 1.0], [352, 99, 1.0], [353, 99, 1.0], ...]
3	[[296, 90, 1.0], [297, 90, 1.0], [293, 91, 1.0], [294, 91, 1.0], [295, 91, 1.0], [296, 91, 1.0], [297, 91, 1.0], [298, 91, 1.0], [299, 91, 1.0], [300, 91, 1.0], [291, 92, 1.0], [292, 92, 1.0], [293, 92, 1.0], [294, 92, 1.0], [295, 92, 1.0], [296, 92, 1.0], [297, 92, 1.0], [298, 92, 1.0], [299, 92, 1.0], [300, 92, 1.0], [301, 92, 1.0], [289, 93, 1.0], [290, 93, 1.0], [291, 93, 1.0], [292, 93, 1.0], [293, 93, 1.0], [294, 93, 1.0], [295, 93, 1.0], [296, 93, 1.0], [297, 93, 1.0], [298, 93, 1.0], [299, 93, 1.0], [300, 93, 1.0], [301, 93, 1.0], [302, 93, 1.0], [288, 94, 1.0], [289, 94, 1.0], [290, 94, 1.0], [291, 94, 1.0], [292, 94, 1.0], [293, 94, 1.0], [294, 94, 1.0], [295, 94, 1.0], [296, 94, 1.0], [297, 94, 1.0], [298, 94, 1.0], [299, 94, 1.0], [300, 94, 1.0], [301, 94, 1.0], [302, 94, 1.0], [287, 95, 1.0], [288, 95, 1.0], [289, 95, 1.0], [290, 95, 1.0], [291, 95, 1.0], [292, 95, 1.0], [293, 95, 1.0], [294, 95, 1.0], [295, 95, 1.0], [296, 95, 1.0], [297, 95, 1.0], [298, 95, 1.0], [299, 95, 1.0], [300, 95, 1.0], [301, 95, 1.0], [302, 95, 1.0], [303, 95, 1.0], [286, 96, 1.0], [287, 96, 1.0], [288, 96, 1.0], [289, 96, 1.0], [290, 96, 1.0], [291, 96, 1.0], [292, 96, 1.0], [293, 96, 1.0], [294, 96, 1.0], [295, 96, 1.0], [296, 96, 1.0], [297, 96, 1.0], [298, 96, 1.0], [299, 96, 1.0], [300, 96, 1.0], [301, 96, 1.0], [302, 96, 1.0], [303, 96, 1.0], [286, 97, 1.0], [287, 97, 1.0], [288, 97, 1.0], [289, 97, 1.0], [290, 97, 1.0], [291, 97, 1.0], [292, 97, 1.0], [293, 97, 1.0], [294, 97, 1.0], [295, 97, 1.0], [296, 97, 1.0], [297, 97, 1.0], [298, 97, 1.0], [299, 97, 1.0], [300, 97, 1.0], ...]

... and 4 more rows.

image_segmentation

plane_segmentations

plane_segmentation_FOV1_channel1

description: Output from segmenting FOV1 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[150, 83, 1.0], [151, 83, 1.0], [152, 83, 1.0], [153, 83, 1.0], [154, 83, 1.0], [155, 83, 1.0], [156, 83, 1.0], [157, 83, 1.0], [158, 83, 1.0], [159, 83, 1.0], [160, 83, 1.0], [161, 83, 1.0], [149, 84, 1.0], [150, 84, 1.0], [151, 84, 1.0], [152, 84, 1.0], [153, 84, 1.0], [154, 84, 1.0], [155, 84, 1.0], [156, 84, 1.0], [157, 84, 1.0], [158, 84, 1.0], [159, 84, 1.0], [160, 84, 1.0], [161, 84, 1.0], [162, 84, 1.0], [163, 84, 1.0], [147, 85, 1.0], [148, 85, 1.0], [149, 85, 1.0], [150, 85, 1.0], [151, 85, 1.0], [152, 85, 1.0], [153, 85, 1.0], [154, 85, 1.0], [155, 85, 1.0], [156, 85, 1.0], [157, 85, 1.0], [158, 85, 1.0], [159, 85, 1.0], [160, 85, 1.0], [161, 85, 1.0], [162, 85, 1.0], [163, 85, 1.0], [164, 85, 1.0], [147, 86, 1.0], [148, 86, 1.0], [149, 86, 1.0], [150, 86, 1.0], [151, 86, 1.0], [152, 86, 1.0], [153, 86, 1.0], [154, 86, 1.0], [155, 86, 1.0], [156, 86, 1.0], [157, 86, 1.0], [158, 86, 1.0], [159, 86, 1.0], [160, 86, 1.0], [161, 86, 1.0], [162, 86, 1.0], [163, 86, 1.0], [164, 86, 1.0], [146, 87, 1.0], [147, 87, 1.0], [148, 87, 1.0], [149, 87, 1.0], [150, 87, 1.0], [151, 87, 1.0], [152, 87, 1.0], [153, 87, 1.0], [154, 87, 1.0], [155, 87, 1.0], [156, 87, 1.0], [157, 87, 1.0], [158, 87, 1.0], [159, 87, 1.0], [160, 87, 1.0], [161, 87, 1.0], [162, 87, 1.0], [163, 87, 1.0], [164, 87, 1.0], [147, 88, 1.0], [148, 88, 1.0], [149, 88, 1.0], [150, 88, 1.0], [151, 88, 1.0], [152, 88, 1.0], [153, 88, 1.0], [154, 88, 1.0], [155, 88, 1.0], [156, 88, 1.0], [157, 88, 1.0], [158, 88, 1.0], [159, 88, 1.0], [160, 88, 1.0], [161, 88, 1.0], [162, 88, 1.0], [163, 88, 1.0], [148, 89, 1.0], ...]
1	[[152, 91, 1.0], [153, 91, 1.0], [154, 91, 1.0], [155, 91, 1.0], [156, 91, 1.0], [150, 92, 1.0], [151, 92, 1.0], [152, 92, 1.0], [153, 92, 1.0], [154, 92, 1.0], [155, 92, 1.0], [156, 92, 1.0], [157, 92, 1.0], [149, 93, 1.0], [150, 93, 1.0], [151, 93, 1.0], [152, 93, 1.0], [153, 93, 1.0], [154, 93, 1.0], [155, 93, 1.0], [156, 93, 1.0], [157, 93, 1.0], [158, 93, 1.0], [148, 94, 1.0], [149, 94, 1.0], [150, 94, 1.0], [151, 94, 1.0], [152, 94, 1.0], [153, 94, 1.0], [154, 94, 1.0], [155, 94, 1.0], [156, 94, 1.0], [157, 94, 1.0], [158, 94, 1.0], [159, 94, 1.0], [148, 95, 1.0], [149, 95, 1.0], [150, 95, 1.0], [151, 95, 1.0], [152, 95, 1.0], [153, 95, 1.0], [154, 95, 1.0], [155, 95, 1.0], [156, 95, 1.0], [157, 95, 1.0], [158, 95, 1.0], [159, 95, 1.0], [160, 95, 1.0], [148, 96, 1.0], [149, 96, 1.0], [150, 96, 1.0], [151, 96, 1.0], [152, 96, 1.0], [153, 96, 1.0], [154, 96, 1.0], [155, 96, 1.0], [156, 96, 1.0], [157, 96, 1.0], [158, 96, 1.0], [159, 96, 1.0], [160, 96, 1.0], [147, 97, 1.0], [148, 97, 1.0], [149, 97, 1.0], [150, 97, 1.0], [151, 97, 1.0], [152, 97, 1.0], [153, 97, 1.0], [154, 97, 1.0], [155, 97, 1.0], [156, 97, 1.0], [157, 97, 1.0], [158, 97, 1.0], [159, 97, 1.0], [160, 97, 1.0], [161, 97, 1.0], [147, 98, 1.0], [148, 98, 1.0], [149, 98, 1.0], [150, 98, 1.0], [151, 98, 1.0], [152, 98, 1.0], [153, 98, 1.0], [154, 98, 1.0], [155, 98, 1.0], [156, 98, 1.0], [157, 98, 1.0], [158, 98, 1.0], [159, 98, 1.0], [160, 98, 1.0], [161, 98, 1.0], [148, 99, 1.0], [149, 99, 1.0], [150, 99, 1.0], [151, 99, 1.0], [152, 99, 1.0], [153, 99, 1.0], [154, 99, 1.0], [155, 99, 1.0], [156, 99, 1.0], ...]
2	[[145, 100, 1.0], [146, 100, 1.0], [147, 100, 1.0], [143, 101, 1.0], [144, 101, 1.0], [145, 101, 1.0], [146, 101, 1.0], [147, 101, 1.0], [148, 101, 1.0], [149, 101, 1.0], [142, 102, 1.0], [143, 102, 1.0], [144, 102, 1.0], [145, 102, 1.0], [146, 102, 1.0], [147, 102, 1.0], [148, 102, 1.0], [149, 102, 1.0], [150, 102, 1.0], [141, 103, 1.0], [142, 103, 1.0], [143, 103, 1.0], [144, 103, 1.0], [145, 103, 1.0], [146, 103, 1.0], [147, 103, 1.0], [148, 103, 1.0], [149, 103, 1.0], [150, 103, 1.0], [151, 103, 1.0], [140, 104, 1.0], [141, 104, 1.0], [142, 104, 1.0], [143, 104, 1.0], [144, 104, 1.0], [145, 104, 1.0], [146, 104, 1.0], [147, 104, 1.0], [148, 104, 1.0], [149, 104, 1.0], [150, 104, 1.0], [151, 104, 1.0], [152, 104, 1.0], [140, 105, 1.0], [141, 105, 1.0], [142, 105, 1.0], [143, 105, 1.0], [144, 105, 1.0], [145, 105, 1.0], [146, 105, 1.0], [147, 105, 1.0], [148, 105, 1.0], [149, 105, 1.0], [150, 105, 1.0], [151, 105, 1.0], [152, 105, 1.0], [139, 106, 1.0], [140, 106, 1.0], [141, 106, 1.0], [142, 106, 1.0], [143, 106, 1.0], [144, 106, 1.0], [145, 106, 1.0], [146, 106, 1.0], [147, 106, 1.0], [148, 106, 1.0], [149, 106, 1.0], [150, 106, 1.0], [151, 106, 1.0], [152, 106, 1.0], [139, 107, 1.0], [140, 107, 1.0], [141, 107, 1.0], [142, 107, 1.0], [143, 107, 1.0], [144, 107, 1.0], [145, 107, 1.0], [146, 107, 1.0], [147, 107, 1.0], [148, 107, 1.0], [149, 107, 1.0], [150, 107, 1.0], [151, 107, 1.0], [152, 107, 1.0], [139, 108, 1.0], [140, 108, 1.0], [141, 108, 1.0], [142, 108, 1.0], [143, 108, 1.0], [144, 108, 1.0], [145, 108, 1.0], [146, 108, 1.0], [147, 108, 1.0], [148, 108, 1.0], [149, 108, 1.0], [150, 108, 1.0], [151, 108, 1.0], [152, 108, 1.0], [140, 109, 1.0], [141, 109, 1.0], ...]
3	[[138, 90, 1.0], [139, 90, 1.0], [140, 90, 1.0], [137, 91, 1.0], [138, 91, 1.0], [139, 91, 1.0], [140, 91, 1.0], [141, 91, 1.0], [136, 92, 1.0], [137, 92, 1.0], [138, 92, 1.0], [139, 92, 1.0], [140, 92, 1.0], [141, 92, 1.0], [142, 92, 1.0], [143, 92, 1.0], [135, 93, 1.0], [136, 93, 1.0], [137, 93, 1.0], [138, 93, 1.0], [139, 93, 1.0], [140, 93, 1.0], [141, 93, 1.0], [142, 93, 1.0], [143, 93, 1.0], [144, 93, 1.0], [134, 94, 1.0], [135, 94, 1.0], [136, 94, 1.0], [137, 94, 1.0], [138, 94, 1.0], [139, 94, 1.0], [140, 94, 1.0], [141, 94, 1.0], [142, 94, 1.0], [143, 94, 1.0], [144, 94, 1.0], [134, 95, 1.0], [135, 95, 1.0], [136, 95, 1.0], [137, 95, 1.0], [138, 95, 1.0], [139, 95, 1.0], [140, 95, 1.0], [141, 95, 1.0], [142, 95, 1.0], [143, 95, 1.0], [144, 95, 1.0], [145, 95, 1.0], [134, 96, 1.0], [135, 96, 1.0], [136, 96, 1.0], [137, 96, 1.0], [138, 96, 1.0], [139, 96, 1.0], [140, 96, 1.0], [141, 96, 1.0], [142, 96, 1.0], [143, 96, 1.0], [144, 96, 1.0], [135, 97, 1.0], [136, 97, 1.0], [137, 97, 1.0], [138, 97, 1.0], [139, 97, 1.0], [140, 97, 1.0], [141, 97, 1.0], [142, 97, 1.0], [143, 97, 1.0], [144, 97, 1.0], [135, 98, 1.0], [136, 98, 1.0], [137, 98, 1.0], [138, 98, 1.0], [139, 98, 1.0], [140, 98, 1.0], [141, 98, 1.0], [142, 98, 1.0], [143, 98, 1.0], [144, 98, 1.0], [136, 99, 1.0], [137, 99, 1.0], [138, 99, 1.0], [139, 99, 1.0], [140, 99, 1.0], [141, 99, 1.0], [142, 99, 1.0], [143, 99, 1.0], [137, 100, 1.0], [138, 100, 1.0], [139, 100, 1.0], [140, 100, 1.0], [141, 100, 1.0], [142, 100, 1.0], [138, 101, 1.0], [139, 101, 1.0], [140, 101, 1.0]]

... and 13 more rows.

plane_segmentation_FOV2_channel1

description: Output from segmenting FOV2 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[159, 51, 1.0], [160, 51, 1.0], [154, 52, 1.0], [155, 52, 1.0], [156, 52, 1.0], [157, 52, 1.0], [158, 52, 1.0], [159, 52, 1.0], [160, 52, 1.0], [161, 52, 1.0], [162, 52, 1.0], [151, 53, 1.0], [152, 53, 1.0], [153, 53, 1.0], [154, 53, 1.0], [155, 53, 1.0], [156, 53, 1.0], [157, 53, 1.0], [158, 53, 1.0], [159, 53, 1.0], [160, 53, 1.0], [161, 53, 1.0], [162, 53, 1.0], [163, 53, 1.0], [150, 54, 1.0], [151, 54, 1.0], [152, 54, 1.0], [153, 54, 1.0], [154, 54, 1.0], [155, 54, 1.0], [156, 54, 1.0], [157, 54, 1.0], [158, 54, 1.0], [159, 54, 1.0], [160, 54, 1.0], [161, 54, 1.0], [162, 54, 1.0], [163, 54, 1.0], [149, 55, 1.0], [150, 55, 1.0], [151, 55, 1.0], [152, 55, 1.0], [153, 55, 1.0], [154, 55, 1.0], [155, 55, 1.0], [156, 55, 1.0], [157, 55, 1.0], [158, 55, 1.0], [159, 55, 1.0], [160, 55, 1.0], [161, 55, 1.0], [162, 55, 1.0], [163, 55, 1.0], [164, 55, 1.0], [149, 56, 1.0], [150, 56, 1.0], [151, 56, 1.0], [152, 56, 1.0], [153, 56, 1.0], [154, 56, 1.0], [155, 56, 1.0], [156, 56, 1.0], [157, 56, 1.0], [158, 56, 1.0], [159, 56, 1.0], [160, 56, 1.0], [161, 56, 1.0], [162, 56, 1.0], [163, 56, 1.0], [164, 56, 1.0], [148, 57, 1.0], [149, 57, 1.0], [150, 57, 1.0], [151, 57, 1.0], [152, 57, 1.0], [153, 57, 1.0], [154, 57, 1.0], [155, 57, 1.0], [156, 57, 1.0], [157, 57, 1.0], [158, 57, 1.0], [159, 57, 1.0], [160, 57, 1.0], [161, 57, 1.0], [162, 57, 1.0], [163, 57, 1.0], [164, 57, 1.0], [165, 57, 1.0], [148, 58, 1.0], [149, 58, 1.0], [150, 58, 1.0], [151, 58, 1.0], [152, 58, 1.0], [153, 58, 1.0], [154, 58, 1.0], [155, 58, 1.0], [156, 58, 1.0], [157, 58, 1.0], [158, 58, 1.0], [159, 58, 1.0], ...]
1	[[136, 63, 1.0], [137, 63, 1.0], [138, 63, 1.0], [139, 63, 1.0], [140, 63, 1.0], [141, 63, 1.0], [135, 64, 1.0], [136, 64, 1.0], [137, 64, 1.0], [138, 64, 1.0], [139, 64, 1.0], [140, 64, 1.0], [141, 64, 1.0], [142, 64, 1.0], [143, 64, 1.0], [134, 65, 1.0], [135, 65, 1.0], [136, 65, 1.0], [137, 65, 1.0], [138, 65, 1.0], [139, 65, 1.0], [140, 65, 1.0], [141, 65, 1.0], [142, 65, 1.0], [143, 65, 1.0], [133, 66, 1.0], [134, 66, 1.0], [135, 66, 1.0], [136, 66, 1.0], [137, 66, 1.0], [138, 66, 1.0], [139, 66, 1.0], [140, 66, 1.0], [141, 66, 1.0], [142, 66, 1.0], [143, 66, 1.0], [144, 66, 1.0], [133, 67, 1.0], [134, 67, 1.0], [135, 67, 1.0], [136, 67, 1.0], [137, 67, 1.0], [138, 67, 1.0], [139, 67, 1.0], [140, 67, 1.0], [141, 67, 1.0], [142, 67, 1.0], [143, 67, 1.0], [144, 67, 1.0], [145, 67, 1.0], [133, 68, 1.0], [134, 68, 1.0], [135, 68, 1.0], [136, 68, 1.0], [137, 68, 1.0], [138, 68, 1.0], [139, 68, 1.0], [140, 68, 1.0], [141, 68, 1.0], [142, 68, 1.0], [143, 68, 1.0], [144, 68, 1.0], [145, 68, 1.0], [132, 69, 1.0], [133, 69, 1.0], [134, 69, 1.0], [135, 69, 1.0], [136, 69, 1.0], [137, 69, 1.0], [138, 69, 1.0], [139, 69, 1.0], [140, 69, 1.0], [141, 69, 1.0], [142, 69, 1.0], [143, 69, 1.0], [144, 69, 1.0], [145, 69, 1.0], [146, 69, 1.0], [132, 70, 1.0], [133, 70, 1.0], [134, 70, 1.0], [135, 70, 1.0], [136, 70, 1.0], [137, 70, 1.0], [138, 70, 1.0], [139, 70, 1.0], [140, 70, 1.0], [141, 70, 1.0], [142, 70, 1.0], [143, 70, 1.0], [144, 70, 1.0], [145, 70, 1.0], [146, 70, 1.0], [147, 70, 1.0], [132, 71, 1.0], [133, 71, 1.0], [134, 71, 1.0], [135, 71, 1.0], [136, 71, 1.0], [137, 71, 1.0], ...]
2	[[144, 96, 1.0], [145, 96, 1.0], [150, 96, 1.0], [151, 96, 1.0], [142, 97, 1.0], [143, 97, 1.0], [144, 97, 1.0], [145, 97, 1.0], [146, 97, 1.0], [147, 97, 1.0], [148, 97, 1.0], [149, 97, 1.0], [150, 97, 1.0], [151, 97, 1.0], [152, 97, 1.0], [140, 98, 1.0], [141, 98, 1.0], [142, 98, 1.0], [143, 98, 1.0], [144, 98, 1.0], [145, 98, 1.0], [146, 98, 1.0], [147, 98, 1.0], [148, 98, 1.0], [149, 98, 1.0], [150, 98, 1.0], [151, 98, 1.0], [152, 98, 1.0], [153, 98, 1.0], [139, 99, 1.0], [140, 99, 1.0], [141, 99, 1.0], [142, 99, 1.0], [143, 99, 1.0], [144, 99, 1.0], [145, 99, 1.0], [146, 99, 1.0], [147, 99, 1.0], [148, 99, 1.0], [149, 99, 1.0], [150, 99, 1.0], [151, 99, 1.0], [152, 99, 1.0], [153, 99, 1.0], [139, 100, 1.0], [140, 100, 1.0], [141, 100, 1.0], [142, 100, 1.0], [143, 100, 1.0], [144, 100, 1.0], [145, 100, 1.0], [146, 100, 1.0], [147, 100, 1.0], [148, 100, 1.0], [149, 100, 1.0], [150, 100, 1.0], [151, 100, 1.0], [152, 100, 1.0], [153, 100, 1.0], [139, 101, 1.0], [140, 101, 1.0], [141, 101, 1.0], [142, 101, 1.0], [143, 101, 1.0], [144, 101, 1.0], [145, 101, 1.0], [146, 101, 1.0], [147, 101, 1.0], [148, 101, 1.0], [149, 101, 1.0], [150, 101, 1.0], [151, 101, 1.0], [152, 101, 1.0], [153, 101, 1.0], [140, 102, 1.0], [141, 102, 1.0], [142, 102, 1.0], [143, 102, 1.0], [144, 102, 1.0], [145, 102, 1.0], [146, 102, 1.0], [147, 102, 1.0], [148, 102, 1.0], [149, 102, 1.0], [150, 102, 1.0], [151, 102, 1.0], [152, 102, 1.0], [142, 103, 1.0], [143, 103, 1.0], [144, 103, 1.0], [145, 103, 1.0], [146, 103, 1.0], [147, 103, 1.0], [148, 103, 1.0], [149, 103, 1.0], [150, 103, 1.0], [151, 103, 1.0], [143, 104, 1.0], [144, 104, 1.0], [145, 104, 1.0], ...]
3	[[146, 85, 1.0], [144, 86, 1.0], [145, 86, 1.0], [146, 86, 1.0], [147, 86, 1.0], [148, 86, 1.0], [149, 86, 1.0], [143, 87, 1.0], [144, 87, 1.0], [145, 87, 1.0], [146, 87, 1.0], [147, 87, 1.0], [148, 87, 1.0], [149, 87, 1.0], [150, 87, 1.0], [142, 88, 1.0], [143, 88, 1.0], [144, 88, 1.0], [145, 88, 1.0], [146, 88, 1.0], [147, 88, 1.0], [148, 88, 1.0], [149, 88, 1.0], [150, 88, 1.0], [142, 89, 1.0], [143, 89, 1.0], [144, 89, 1.0], [145, 89, 1.0], [146, 89, 1.0], [147, 89, 1.0], [148, 89, 1.0], [149, 89, 1.0], [150, 89, 1.0], [151, 89, 1.0], [141, 90, 1.0], [142, 90, 1.0], [143, 90, 1.0], [144, 90, 1.0], [145, 90, 1.0], [146, 90, 1.0], [147, 90, 1.0], [148, 90, 1.0], [149, 90, 1.0], [150, 90, 1.0], [151, 90, 1.0], [152, 90, 1.0], [141, 91, 1.0], [142, 91, 1.0], [143, 91, 1.0], [144, 91, 1.0], [145, 91, 1.0], [146, 91, 1.0], [147, 91, 1.0], [148, 91, 1.0], [149, 91, 1.0], [150, 91, 1.0], [151, 91, 1.0], [152, 91, 1.0], [142, 92, 1.0], [143, 92, 1.0], [144, 92, 1.0], [145, 92, 1.0], [146, 92, 1.0], [147, 92, 1.0], [148, 92, 1.0], [149, 92, 1.0], [150, 92, 1.0], [151, 92, 1.0], [152, 92, 1.0], [142, 93, 1.0], [143, 93, 1.0], [144, 93, 1.0], [145, 93, 1.0], [146, 93, 1.0], [147, 93, 1.0], [148, 93, 1.0], [149, 93, 1.0], [150, 93, 1.0], [151, 93, 1.0], [152, 93, 1.0], [143, 94, 1.0], [144, 94, 1.0], [145, 94, 1.0], [146, 94, 1.0], [147, 94, 1.0], [148, 94, 1.0], [149, 94, 1.0], [150, 94, 1.0], [151, 94, 1.0], [144, 95, 1.0], [145, 95, 1.0], [146, 95, 1.0], [147, 95, 1.0], [148, 95, 1.0], [149, 95, 1.0], [150, 95, 1.0], [151, 95, 1.0], [146, 96, 1.0], [147, 96, 1.0], [148, 96, 1.0], ...]

... and 44 more rows.

plane_segmentation_FOV3_channel1

description: Output from segmenting FOV3 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[335, 89, 1.0], [332, 90, 1.0], [333, 90, 1.0], [334, 90, 1.0], [335, 90, 1.0], [336, 90, 1.0], [337, 90, 1.0], [338, 90, 1.0], [339, 90, 1.0], [327, 91, 1.0], [328, 91, 1.0], [329, 91, 1.0], [330, 91, 1.0], [331, 91, 1.0], [332, 91, 1.0], [333, 91, 1.0], [334, 91, 1.0], [335, 91, 1.0], [336, 91, 1.0], [337, 91, 1.0], [338, 91, 1.0], [339, 91, 1.0], [340, 91, 1.0], [326, 92, 1.0], [327, 92, 1.0], [328, 92, 1.0], [329, 92, 1.0], [330, 92, 1.0], [331, 92, 1.0], [332, 92, 1.0], [333, 92, 1.0], [334, 92, 1.0], [335, 92, 1.0], [336, 92, 1.0], [337, 92, 1.0], [338, 92, 1.0], [339, 92, 1.0], [340, 92, 1.0], [341, 92, 1.0], [324, 93, 1.0], [325, 93, 1.0], [326, 93, 1.0], [327, 93, 1.0], [328, 93, 1.0], [329, 93, 1.0], [330, 93, 1.0], [331, 93, 1.0], [332, 93, 1.0], [333, 93, 1.0], [334, 93, 1.0], [335, 93, 1.0], [336, 93, 1.0], [337, 93, 1.0], [338, 93, 1.0], [339, 93, 1.0], [340, 93, 1.0], [341, 93, 1.0], [342, 93, 1.0], [323, 94, 1.0], [324, 94, 1.0], [325, 94, 1.0], [326, 94, 1.0], [327, 94, 1.0], [328, 94, 1.0], [329, 94, 1.0], [330, 94, 1.0], [331, 94, 1.0], [332, 94, 1.0], [333, 94, 1.0], [334, 94, 1.0], [335, 94, 1.0], [336, 94, 1.0], [337, 94, 1.0], [338, 94, 1.0], [339, 94, 1.0], [340, 94, 1.0], [341, 94, 1.0], [342, 94, 1.0], [322, 95, 1.0], [323, 95, 1.0], [324, 95, 1.0], [325, 95, 1.0], [326, 95, 1.0], [327, 95, 1.0], [328, 95, 1.0], [329, 95, 1.0], [330, 95, 1.0], [331, 95, 1.0], [332, 95, 1.0], [333, 95, 1.0], [334, 95, 1.0], [335, 95, 1.0], [336, 95, 1.0], [337, 95, 1.0], [338, 95, 1.0], [339, 95, 1.0], [340, 95, 1.0], [341, 95, 1.0], [342, 95, 1.0], [343, 95, 1.0], ...]
1	[[325, 77, 1.0], [326, 77, 1.0], [327, 77, 1.0], [328, 77, 1.0], [329, 77, 1.0], [323, 78, 1.0], [324, 78, 1.0], [325, 78, 1.0], [326, 78, 1.0], [327, 78, 1.0], [328, 78, 1.0], [329, 78, 1.0], [330, 78, 1.0], [321, 79, 1.0], [322, 79, 1.0], [323, 79, 1.0], [324, 79, 1.0], [325, 79, 1.0], [326, 79, 1.0], [327, 79, 1.0], [328, 79, 1.0], [329, 79, 1.0], [330, 79, 1.0], [331, 79, 1.0], [320, 80, 1.0], [321, 80, 1.0], [322, 80, 1.0], [323, 80, 1.0], [324, 80, 1.0], [325, 80, 1.0], [326, 80, 1.0], [327, 80, 1.0], [328, 80, 1.0], [329, 80, 1.0], [330, 80, 1.0], [331, 80, 1.0], [332, 80, 1.0], [319, 81, 1.0], [320, 81, 1.0], [321, 81, 1.0], [322, 81, 1.0], [323, 81, 1.0], [324, 81, 1.0], [325, 81, 1.0], [326, 81, 1.0], [327, 81, 1.0], [328, 81, 1.0], [329, 81, 1.0], [330, 81, 1.0], [331, 81, 1.0], [332, 81, 1.0], [319, 82, 1.0], [320, 82, 1.0], [321, 82, 1.0], [322, 82, 1.0], [323, 82, 1.0], [324, 82, 1.0], [325, 82, 1.0], [326, 82, 1.0], [327, 82, 1.0], [328, 82, 1.0], [329, 82, 1.0], [330, 82, 1.0], [331, 82, 1.0], [332, 82, 1.0], [333, 82, 1.0], [318, 83, 1.0], [319, 83, 1.0], [320, 83, 1.0], [321, 83, 1.0], [322, 83, 1.0], [323, 83, 1.0], [324, 83, 1.0], [325, 83, 1.0], [326, 83, 1.0], [327, 83, 1.0], [328, 83, 1.0], [329, 83, 1.0], [330, 83, 1.0], [331, 83, 1.0], [332, 83, 1.0], [333, 83, 1.0], [334, 83, 1.0], [318, 84, 1.0], [319, 84, 1.0], [320, 84, 1.0], [321, 84, 1.0], [322, 84, 1.0], [323, 84, 1.0], [324, 84, 1.0], [325, 84, 1.0], [326, 84, 1.0], [327, 84, 1.0], [328, 84, 1.0], [329, 84, 1.0], [330, 84, 1.0], [331, 84, 1.0], [332, 84, 1.0], [333, 84, 1.0], [334, 84, 1.0], ...]
2	[[351, 92, 1.0], [352, 92, 1.0], [353, 92, 1.0], [354, 92, 1.0], [355, 92, 1.0], [349, 93, 1.0], [350, 93, 1.0], [351, 93, 1.0], [352, 93, 1.0], [353, 93, 1.0], [354, 93, 1.0], [355, 93, 1.0], [356, 93, 1.0], [357, 93, 1.0], [347, 94, 1.0], [348, 94, 1.0], [349, 94, 1.0], [350, 94, 1.0], [351, 94, 1.0], [352, 94, 1.0], [353, 94, 1.0], [354, 94, 1.0], [355, 94, 1.0], [356, 94, 1.0], [357, 94, 1.0], [358, 94, 1.0], [347, 95, 1.0], [348, 95, 1.0], [349, 95, 1.0], [350, 95, 1.0], [351, 95, 1.0], [352, 95, 1.0], [353, 95, 1.0], [354, 95, 1.0], [355, 95, 1.0], [356, 95, 1.0], [357, 95, 1.0], [358, 95, 1.0], [359, 95, 1.0], [346, 96, 1.0], [347, 96, 1.0], [348, 96, 1.0], [349, 96, 1.0], [350, 96, 1.0], [351, 96, 1.0], [352, 96, 1.0], [353, 96, 1.0], [354, 96, 1.0], [355, 96, 1.0], [356, 96, 1.0], [357, 96, 1.0], [358, 96, 1.0], [359, 96, 1.0], [360, 96, 1.0], [345, 97, 1.0], [346, 97, 1.0], [347, 97, 1.0], [348, 97, 1.0], [349, 97, 1.0], [350, 97, 1.0], [351, 97, 1.0], [352, 97, 1.0], [353, 97, 1.0], [354, 97, 1.0], [355, 97, 1.0], [356, 97, 1.0], [357, 97, 1.0], [358, 97, 1.0], [359, 97, 1.0], [360, 97, 1.0], [361, 97, 1.0], [344, 98, 1.0], [345, 98, 1.0], [346, 98, 1.0], [347, 98, 1.0], [348, 98, 1.0], [349, 98, 1.0], [350, 98, 1.0], [351, 98, 1.0], [352, 98, 1.0], [353, 98, 1.0], [354, 98, 1.0], [355, 98, 1.0], [356, 98, 1.0], [357, 98, 1.0], [358, 98, 1.0], [359, 98, 1.0], [360, 98, 1.0], [361, 98, 1.0], [362, 98, 1.0], [344, 99, 1.0], [345, 99, 1.0], [346, 99, 1.0], [347, 99, 1.0], [348, 99, 1.0], [349, 99, 1.0], [350, 99, 1.0], [351, 99, 1.0], [352, 99, 1.0], [353, 99, 1.0], ...]
3	[[296, 90, 1.0], [297, 90, 1.0], [293, 91, 1.0], [294, 91, 1.0], [295, 91, 1.0], [296, 91, 1.0], [297, 91, 1.0], [298, 91, 1.0], [299, 91, 1.0], [300, 91, 1.0], [291, 92, 1.0], [292, 92, 1.0], [293, 92, 1.0], [294, 92, 1.0], [295, 92, 1.0], [296, 92, 1.0], [297, 92, 1.0], [298, 92, 1.0], [299, 92, 1.0], [300, 92, 1.0], [301, 92, 1.0], [289, 93, 1.0], [290, 93, 1.0], [291, 93, 1.0], [292, 93, 1.0], [293, 93, 1.0], [294, 93, 1.0], [295, 93, 1.0], [296, 93, 1.0], [297, 93, 1.0], [298, 93, 1.0], [299, 93, 1.0], [300, 93, 1.0], [301, 93, 1.0], [302, 93, 1.0], [288, 94, 1.0], [289, 94, 1.0], [290, 94, 1.0], [291, 94, 1.0], [292, 94, 1.0], [293, 94, 1.0], [294, 94, 1.0], [295, 94, 1.0], [296, 94, 1.0], [297, 94, 1.0], [298, 94, 1.0], [299, 94, 1.0], [300, 94, 1.0], [301, 94, 1.0], [302, 94, 1.0], [287, 95, 1.0], [288, 95, 1.0], [289, 95, 1.0], [290, 95, 1.0], [291, 95, 1.0], [292, 95, 1.0], [293, 95, 1.0], [294, 95, 1.0], [295, 95, 1.0], [296, 95, 1.0], [297, 95, 1.0], [298, 95, 1.0], [299, 95, 1.0], [300, 95, 1.0], [301, 95, 1.0], [302, 95, 1.0], [303, 95, 1.0], [286, 96, 1.0], [287, 96, 1.0], [288, 96, 1.0], [289, 96, 1.0], [290, 96, 1.0], [291, 96, 1.0], [292, 96, 1.0], [293, 96, 1.0], [294, 96, 1.0], [295, 96, 1.0], [296, 96, 1.0], [297, 96, 1.0], [298, 96, 1.0], [299, 96, 1.0], [300, 96, 1.0], [301, 96, 1.0], [302, 96, 1.0], [303, 96, 1.0], [286, 97, 1.0], [287, 97, 1.0], [288, 97, 1.0], [289, 97, 1.0], [290, 97, 1.0], [291, 97, 1.0], [292, 97, 1.0], [293, 97, 1.0], [294, 97, 1.0], [295, 97, 1.0], [296, 97, 1.0], [297, 97, 1.0], [298, 97, 1.0], [299, 97, 1.0], [300, 97, 1.0], ...]

... and 4 more rows.

devices

two_photon_microscope

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

imaging_planes

imaging_plane_channel1

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

intervals

trials

description: experimental trials

table

	start_time	stop_time	odorant	concentration	solution_date
id
0	59.2888	60.2928	Methyl Salicylate	0.1	2022-05-17
1	60.2966	61.2995	Methyl Salicylate	0.1	2022-05-17
2	61.3034	62.3067	Methyl Salicylate	0.1	2022-05-17
3	62.3106	63.3130	Methyl Salicylate	0.1	2022-05-17

... and 496 more rows.

subject

age__reference: birth

description: ArenkielMouse

sex: M

species: Mus musculus

subject_id: 134

date_of_birth

2021-07-15 00:00:00+02:00

trials

description: experimental trials

table

	start_time	stop_time	odorant	concentration	solution_date
id
0	59.2888	60.2928	Methyl Salicylate	0.1	2022-05-17
1	60.2966	61.2995	Methyl Salicylate	0.1	2022-05-17
2	61.3034	62.3067	Methyl Salicylate	0.1	2022-05-17
3	62.3106	63.3130	Methyl Salicylate	0.1	2022-05-17

... and 496 more rows.

experiment_description: Awake mice were head fixed on a running wheel and presented with odors. A meso-scale two-photon microscope was used to image glomerular activity in the olfactory bulb.

session_id: 22

lab: Reimer-Arenkiel

institution: Baylor College of Medicine

Importantly, the session start time is the reference time for all timestamps in the file. For instance, an event with a timestamp of 0 in the file means the event occurred exactly at the session start time.

The session_start_time is extracted from all_sessions.Session datajoint table.

In [5]:

nwbfile.session_start_time

nwbfile.session_start_time

Out[5]:

datetime.datetime(2022, 7, 21, 0, 0, tzinfo=tzoffset(None, -18000))

The experiment description or the session description can be easily accessed with nwbfile.experiment_description and nwbfile.session_description

In [6]:

nwbfile.experiment_description

nwbfile.experiment_description

Out[6]:

'Awake mice were head fixed on a running wheel and presented with odors. A meso-scale two-photon microscope was used to image glomerular activity in the olfactory bulb.'

In [7]:

nwbfile.session_description

nwbfile.session_description

Out[7]:

'In this study, we explore odor-evoked activity representation in the olfactory bulb (OB) and how odor responses enable odor discrimination. Contrary to some previously cited theories that suggest a sparse representation, we hypothesize a more dense representation during odor presentation. A key question is how odors are reliably encoded in OB activity patterns, and how these patterns contribute to early odor processing. To address this problem, we recorded population level odor responses from the mouse OB with mesoscale two photon calcium imaging and applied machine learning techniques to suggest a model in which sparse coding is largely sufficient for olfaction, but redundant information may make odor coding more robust across different variables.'

Access subject ¶

This section demonstrates how to access the Subject field in an NWB file.

The Subject field can be accessed as nwbfile.subject.

In [8]:

nwbfile.subject

nwbfile.subject

Out[8]:

subject (Subject)

age__reference: birth

description: ArenkielMouse

sex: M

species: Mus musculus

subject_id: 134

date_of_birth

2021-07-15 00:00:00+02:00

Access TwoPhoton Imaging¶

This section demonstraces how to access the raw Two Photon imaging data.

NWB organizes data into different groups depending on the type of data. Groups can be thought of as folders within the file. Here are some of the groups within an NWBFile and the types of data they are intended to store:

• acquisition: raw, acquired data that should never change
• processing: processed data, typically the results of preprocessing algorithms and could change

Raw TwoPhoton Imaging ¶

The raw TwoPhoton imaging data is stored in pynwb.ophys.TwoPhotonSeries objects (for each channel and plane separately) which is added to nwbfile.acquisition.

The data in TwoPhotonSeries is stored as a three dimensional array: the first dimension is time (frame), the second and third dimensions represent x and y (width by height).

In [9]:

photon_series_names = [photon_series_name for photon_series_name in nwbfile.acquisition.keys() if "two_photon_series" in photon_series_name]
print(photon_series_names)

photon_series_names = [photon_series_name for photon_series_name in nwbfile.acquisition.keys() if "two_photon_series" in photon_series_name] print(photon_series_names)

['two_photon_series_FOV1_channel1', 'two_photon_series_FOV2_channel1', 'two_photon_series_FOV3_channel1']

In [10]:

# Visualize the imaging data.
from matplotlib import pyplot as plt
fig, axs = plt.subplots(nrows=len(photon_series_names), ncols=1, sharex=True, sharey=True)
for ps_index,ps_name in enumerate(photon_series_names):    
    photon_series = nwbfile.acquisition[ps_name]
    axs[ps_index].imshow(photon_series.data[70], cmap="binary")
    axs[ps_index].set_title(ps_name.replace("_"," "))
plt.show()

# Visualize the imaging data. from matplotlib import pyplot as plt fig, axs = plt.subplots(nrows=len(photon_series_names), ncols=1, sharex=True, sharey=True) for ps_index,ps_name in enumerate(photon_series_names): photon_series = nwbfile.acquisition[ps_name] axs[ps_index].imshow(photon_series.data[70], cmap="binary") axs[ps_index].set_title(ps_name.replace("_"," ")) plt.show()

Imaging metadata include information on the device used to acquire the image and the optical channel specs contained in the imaging plane object

In [11]:

nwbfile.devices["two_photon_microscope"]

nwbfile.devices["two_photon_microscope"]

Out[11]:

two_photon_microscope (Device)

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

In [12]:

nwbfile.imaging_planes["imaging_plane_channel1"]

nwbfile.imaging_planes["imaging_plane_channel1"]

Out[12]:

imaging_plane_channel1 (ImagingPlane)

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

Accessing the segmentation data¶

The segmentation output for the Two Photon Imaging data is stored in nwbfile.processing["ophys"].

In NWB, the PlaneSegmentation class stores the detected regions of interest in the TwoPhotonSeries data. The ImageSegmentation can contain multiple PlaneSegmentation tables, so that we can store results of different segmentation algorithms or different segmentation classes.

We can access the plane segmentation for the TwoPhotonSeries data as nwbfile.processing["ophys"]["ImageSegmentation"]["PlaneSegmentationChannel_number_Plane_number_"].

In [13]:

nwbfile.processing["ophys"]

nwbfile.processing["ophys"]

Out[13]:

ophys (ProcessingModule)

description: ophys data processing

data_interfaces

average_images

description: Average image of from SummaryImages.Average table.

images

average_image_FOV1_channel1

average_image_FOV2_channel1

average_image_FOV3_channel1

correlation_images

description: Correlation image from SummaryImages.Correlation table.

images

correlation_image_FOV1_channel1

correlation_image_FOV2_channel1

correlation_image_FOV3_channel1

fluorescence

roi_response_series

fluorescence_FOV1_channel1

resolution: -1.0

comments: no comments

description: Fluorescence traces from FOV1 Channel1

conversion: 1.0

offset: 0.0

unit: n.a.

data

timestamps

timestamps_unit: seconds

interval: 1

rois

description: all ROIs

table

description: Output from segmenting FOV1 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[150, 83, 1.0], [151, 83, 1.0], [152, 83, 1.0], [153, 83, 1.0], [154, 83, 1.0], [155, 83, 1.0], [156, 83, 1.0], [157, 83, 1.0], [158, 83, 1.0], [159, 83, 1.0], [160, 83, 1.0], [161, 83, 1.0], [149, 84, 1.0], [150, 84, 1.0], [151, 84, 1.0], [152, 84, 1.0], [153, 84, 1.0], [154, 84, 1.0], [155, 84, 1.0], [156, 84, 1.0], [157, 84, 1.0], [158, 84, 1.0], [159, 84, 1.0], [160, 84, 1.0], [161, 84, 1.0], [162, 84, 1.0], [163, 84, 1.0], [147, 85, 1.0], [148, 85, 1.0], [149, 85, 1.0], [150, 85, 1.0], [151, 85, 1.0], [152, 85, 1.0], [153, 85, 1.0], [154, 85, 1.0], [155, 85, 1.0], [156, 85, 1.0], [157, 85, 1.0], [158, 85, 1.0], [159, 85, 1.0], [160, 85, 1.0], [161, 85, 1.0], [162, 85, 1.0], [163, 85, 1.0], [164, 85, 1.0], [147, 86, 1.0], [148, 86, 1.0], [149, 86, 1.0], [150, 86, 1.0], [151, 86, 1.0], [152, 86, 1.0], [153, 86, 1.0], [154, 86, 1.0], [155, 86, 1.0], [156, 86, 1.0], [157, 86, 1.0], [158, 86, 1.0], [159, 86, 1.0], [160, 86, 1.0], [161, 86, 1.0], [162, 86, 1.0], [163, 86, 1.0], [164, 86, 1.0], [146, 87, 1.0], [147, 87, 1.0], [148, 87, 1.0], [149, 87, 1.0], [150, 87, 1.0], [151, 87, 1.0], [152, 87, 1.0], [153, 87, 1.0], [154, 87, 1.0], [155, 87, 1.0], [156, 87, 1.0], [157, 87, 1.0], [158, 87, 1.0], [159, 87, 1.0], [160, 87, 1.0], [161, 87, 1.0], [162, 87, 1.0], [163, 87, 1.0], [164, 87, 1.0], [147, 88, 1.0], [148, 88, 1.0], [149, 88, 1.0], [150, 88, 1.0], [151, 88, 1.0], [152, 88, 1.0], [153, 88, 1.0], [154, 88, 1.0], [155, 88, 1.0], [156, 88, 1.0], [157, 88, 1.0], [158, 88, 1.0], [159, 88, 1.0], [160, 88, 1.0], [161, 88, 1.0], [162, 88, 1.0], [163, 88, 1.0], [148, 89, 1.0], ...]
1	[[152, 91, 1.0], [153, 91, 1.0], [154, 91, 1.0], [155, 91, 1.0], [156, 91, 1.0], [150, 92, 1.0], [151, 92, 1.0], [152, 92, 1.0], [153, 92, 1.0], [154, 92, 1.0], [155, 92, 1.0], [156, 92, 1.0], [157, 92, 1.0], [149, 93, 1.0], [150, 93, 1.0], [151, 93, 1.0], [152, 93, 1.0], [153, 93, 1.0], [154, 93, 1.0], [155, 93, 1.0], [156, 93, 1.0], [157, 93, 1.0], [158, 93, 1.0], [148, 94, 1.0], [149, 94, 1.0], [150, 94, 1.0], [151, 94, 1.0], [152, 94, 1.0], [153, 94, 1.0], [154, 94, 1.0], [155, 94, 1.0], [156, 94, 1.0], [157, 94, 1.0], [158, 94, 1.0], [159, 94, 1.0], [148, 95, 1.0], [149, 95, 1.0], [150, 95, 1.0], [151, 95, 1.0], [152, 95, 1.0], [153, 95, 1.0], [154, 95, 1.0], [155, 95, 1.0], [156, 95, 1.0], [157, 95, 1.0], [158, 95, 1.0], [159, 95, 1.0], [160, 95, 1.0], [148, 96, 1.0], [149, 96, 1.0], [150, 96, 1.0], [151, 96, 1.0], [152, 96, 1.0], [153, 96, 1.0], [154, 96, 1.0], [155, 96, 1.0], [156, 96, 1.0], [157, 96, 1.0], [158, 96, 1.0], [159, 96, 1.0], [160, 96, 1.0], [147, 97, 1.0], [148, 97, 1.0], [149, 97, 1.0], [150, 97, 1.0], [151, 97, 1.0], [152, 97, 1.0], [153, 97, 1.0], [154, 97, 1.0], [155, 97, 1.0], [156, 97, 1.0], [157, 97, 1.0], [158, 97, 1.0], [159, 97, 1.0], [160, 97, 1.0], [161, 97, 1.0], [147, 98, 1.0], [148, 98, 1.0], [149, 98, 1.0], [150, 98, 1.0], [151, 98, 1.0], [152, 98, 1.0], [153, 98, 1.0], [154, 98, 1.0], [155, 98, 1.0], [156, 98, 1.0], [157, 98, 1.0], [158, 98, 1.0], [159, 98, 1.0], [160, 98, 1.0], [161, 98, 1.0], [148, 99, 1.0], [149, 99, 1.0], [150, 99, 1.0], [151, 99, 1.0], [152, 99, 1.0], [153, 99, 1.0], [154, 99, 1.0], [155, 99, 1.0], [156, 99, 1.0], ...]
2	[[145, 100, 1.0], [146, 100, 1.0], [147, 100, 1.0], [143, 101, 1.0], [144, 101, 1.0], [145, 101, 1.0], [146, 101, 1.0], [147, 101, 1.0], [148, 101, 1.0], [149, 101, 1.0], [142, 102, 1.0], [143, 102, 1.0], [144, 102, 1.0], [145, 102, 1.0], [146, 102, 1.0], [147, 102, 1.0], [148, 102, 1.0], [149, 102, 1.0], [150, 102, 1.0], [141, 103, 1.0], [142, 103, 1.0], [143, 103, 1.0], [144, 103, 1.0], [145, 103, 1.0], [146, 103, 1.0], [147, 103, 1.0], [148, 103, 1.0], [149, 103, 1.0], [150, 103, 1.0], [151, 103, 1.0], [140, 104, 1.0], [141, 104, 1.0], [142, 104, 1.0], [143, 104, 1.0], [144, 104, 1.0], [145, 104, 1.0], [146, 104, 1.0], [147, 104, 1.0], [148, 104, 1.0], [149, 104, 1.0], [150, 104, 1.0], [151, 104, 1.0], [152, 104, 1.0], [140, 105, 1.0], [141, 105, 1.0], [142, 105, 1.0], [143, 105, 1.0], [144, 105, 1.0], [145, 105, 1.0], [146, 105, 1.0], [147, 105, 1.0], [148, 105, 1.0], [149, 105, 1.0], [150, 105, 1.0], [151, 105, 1.0], [152, 105, 1.0], [139, 106, 1.0], [140, 106, 1.0], [141, 106, 1.0], [142, 106, 1.0], [143, 106, 1.0], [144, 106, 1.0], [145, 106, 1.0], [146, 106, 1.0], [147, 106, 1.0], [148, 106, 1.0], [149, 106, 1.0], [150, 106, 1.0], [151, 106, 1.0], [152, 106, 1.0], [139, 107, 1.0], [140, 107, 1.0], [141, 107, 1.0], [142, 107, 1.0], [143, 107, 1.0], [144, 107, 1.0], [145, 107, 1.0], [146, 107, 1.0], [147, 107, 1.0], [148, 107, 1.0], [149, 107, 1.0], [150, 107, 1.0], [151, 107, 1.0], [152, 107, 1.0], [139, 108, 1.0], [140, 108, 1.0], [141, 108, 1.0], [142, 108, 1.0], [143, 108, 1.0], [144, 108, 1.0], [145, 108, 1.0], [146, 108, 1.0], [147, 108, 1.0], [148, 108, 1.0], [149, 108, 1.0], [150, 108, 1.0], [151, 108, 1.0], [152, 108, 1.0], [140, 109, 1.0], [141, 109, 1.0], ...]
3	[[138, 90, 1.0], [139, 90, 1.0], [140, 90, 1.0], [137, 91, 1.0], [138, 91, 1.0], [139, 91, 1.0], [140, 91, 1.0], [141, 91, 1.0], [136, 92, 1.0], [137, 92, 1.0], [138, 92, 1.0], [139, 92, 1.0], [140, 92, 1.0], [141, 92, 1.0], [142, 92, 1.0], [143, 92, 1.0], [135, 93, 1.0], [136, 93, 1.0], [137, 93, 1.0], [138, 93, 1.0], [139, 93, 1.0], [140, 93, 1.0], [141, 93, 1.0], [142, 93, 1.0], [143, 93, 1.0], [144, 93, 1.0], [134, 94, 1.0], [135, 94, 1.0], [136, 94, 1.0], [137, 94, 1.0], [138, 94, 1.0], [139, 94, 1.0], [140, 94, 1.0], [141, 94, 1.0], [142, 94, 1.0], [143, 94, 1.0], [144, 94, 1.0], [134, 95, 1.0], [135, 95, 1.0], [136, 95, 1.0], [137, 95, 1.0], [138, 95, 1.0], [139, 95, 1.0], [140, 95, 1.0], [141, 95, 1.0], [142, 95, 1.0], [143, 95, 1.0], [144, 95, 1.0], [145, 95, 1.0], [134, 96, 1.0], [135, 96, 1.0], [136, 96, 1.0], [137, 96, 1.0], [138, 96, 1.0], [139, 96, 1.0], [140, 96, 1.0], [141, 96, 1.0], [142, 96, 1.0], [143, 96, 1.0], [144, 96, 1.0], [135, 97, 1.0], [136, 97, 1.0], [137, 97, 1.0], [138, 97, 1.0], [139, 97, 1.0], [140, 97, 1.0], [141, 97, 1.0], [142, 97, 1.0], [143, 97, 1.0], [144, 97, 1.0], [135, 98, 1.0], [136, 98, 1.0], [137, 98, 1.0], [138, 98, 1.0], [139, 98, 1.0], [140, 98, 1.0], [141, 98, 1.0], [142, 98, 1.0], [143, 98, 1.0], [144, 98, 1.0], [136, 99, 1.0], [137, 99, 1.0], [138, 99, 1.0], [139, 99, 1.0], [140, 99, 1.0], [141, 99, 1.0], [142, 99, 1.0], [143, 99, 1.0], [137, 100, 1.0], [138, 100, 1.0], [139, 100, 1.0], [140, 100, 1.0], [141, 100, 1.0], [142, 100, 1.0], [138, 101, 1.0], [139, 101, 1.0], [140, 101, 1.0]]

... and 13 more rows.

fluorescence_FOV2_channel1

resolution: -1.0

comments: no comments

description: Fluorescence traces from FOV2 Channel1

conversion: 1.0

offset: 0.0

unit: n.a.

data

timestamps

timestamps_unit: seconds

interval: 1

rois

description: all ROIs

table

description: Output from segmenting FOV2 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[159, 51, 1.0], [160, 51, 1.0], [154, 52, 1.0], [155, 52, 1.0], [156, 52, 1.0], [157, 52, 1.0], [158, 52, 1.0], [159, 52, 1.0], [160, 52, 1.0], [161, 52, 1.0], [162, 52, 1.0], [151, 53, 1.0], [152, 53, 1.0], [153, 53, 1.0], [154, 53, 1.0], [155, 53, 1.0], [156, 53, 1.0], [157, 53, 1.0], [158, 53, 1.0], [159, 53, 1.0], [160, 53, 1.0], [161, 53, 1.0], [162, 53, 1.0], [163, 53, 1.0], [150, 54, 1.0], [151, 54, 1.0], [152, 54, 1.0], [153, 54, 1.0], [154, 54, 1.0], [155, 54, 1.0], [156, 54, 1.0], [157, 54, 1.0], [158, 54, 1.0], [159, 54, 1.0], [160, 54, 1.0], [161, 54, 1.0], [162, 54, 1.0], [163, 54, 1.0], [149, 55, 1.0], [150, 55, 1.0], [151, 55, 1.0], [152, 55, 1.0], [153, 55, 1.0], [154, 55, 1.0], [155, 55, 1.0], [156, 55, 1.0], [157, 55, 1.0], [158, 55, 1.0], [159, 55, 1.0], [160, 55, 1.0], [161, 55, 1.0], [162, 55, 1.0], [163, 55, 1.0], [164, 55, 1.0], [149, 56, 1.0], [150, 56, 1.0], [151, 56, 1.0], [152, 56, 1.0], [153, 56, 1.0], [154, 56, 1.0], [155, 56, 1.0], [156, 56, 1.0], [157, 56, 1.0], [158, 56, 1.0], [159, 56, 1.0], [160, 56, 1.0], [161, 56, 1.0], [162, 56, 1.0], [163, 56, 1.0], [164, 56, 1.0], [148, 57, 1.0], [149, 57, 1.0], [150, 57, 1.0], [151, 57, 1.0], [152, 57, 1.0], [153, 57, 1.0], [154, 57, 1.0], [155, 57, 1.0], [156, 57, 1.0], [157, 57, 1.0], [158, 57, 1.0], [159, 57, 1.0], [160, 57, 1.0], [161, 57, 1.0], [162, 57, 1.0], [163, 57, 1.0], [164, 57, 1.0], [165, 57, 1.0], [148, 58, 1.0], [149, 58, 1.0], [150, 58, 1.0], [151, 58, 1.0], [152, 58, 1.0], [153, 58, 1.0], [154, 58, 1.0], [155, 58, 1.0], [156, 58, 1.0], [157, 58, 1.0], [158, 58, 1.0], [159, 58, 1.0], ...]
1	[[136, 63, 1.0], [137, 63, 1.0], [138, 63, 1.0], [139, 63, 1.0], [140, 63, 1.0], [141, 63, 1.0], [135, 64, 1.0], [136, 64, 1.0], [137, 64, 1.0], [138, 64, 1.0], [139, 64, 1.0], [140, 64, 1.0], [141, 64, 1.0], [142, 64, 1.0], [143, 64, 1.0], [134, 65, 1.0], [135, 65, 1.0], [136, 65, 1.0], [137, 65, 1.0], [138, 65, 1.0], [139, 65, 1.0], [140, 65, 1.0], [141, 65, 1.0], [142, 65, 1.0], [143, 65, 1.0], [133, 66, 1.0], [134, 66, 1.0], [135, 66, 1.0], [136, 66, 1.0], [137, 66, 1.0], [138, 66, 1.0], [139, 66, 1.0], [140, 66, 1.0], [141, 66, 1.0], [142, 66, 1.0], [143, 66, 1.0], [144, 66, 1.0], [133, 67, 1.0], [134, 67, 1.0], [135, 67, 1.0], [136, 67, 1.0], [137, 67, 1.0], [138, 67, 1.0], [139, 67, 1.0], [140, 67, 1.0], [141, 67, 1.0], [142, 67, 1.0], [143, 67, 1.0], [144, 67, 1.0], [145, 67, 1.0], [133, 68, 1.0], [134, 68, 1.0], [135, 68, 1.0], [136, 68, 1.0], [137, 68, 1.0], [138, 68, 1.0], [139, 68, 1.0], [140, 68, 1.0], [141, 68, 1.0], [142, 68, 1.0], [143, 68, 1.0], [144, 68, 1.0], [145, 68, 1.0], [132, 69, 1.0], [133, 69, 1.0], [134, 69, 1.0], [135, 69, 1.0], [136, 69, 1.0], [137, 69, 1.0], [138, 69, 1.0], [139, 69, 1.0], [140, 69, 1.0], [141, 69, 1.0], [142, 69, 1.0], [143, 69, 1.0], [144, 69, 1.0], [145, 69, 1.0], [146, 69, 1.0], [132, 70, 1.0], [133, 70, 1.0], [134, 70, 1.0], [135, 70, 1.0], [136, 70, 1.0], [137, 70, 1.0], [138, 70, 1.0], [139, 70, 1.0], [140, 70, 1.0], [141, 70, 1.0], [142, 70, 1.0], [143, 70, 1.0], [144, 70, 1.0], [145, 70, 1.0], [146, 70, 1.0], [147, 70, 1.0], [132, 71, 1.0], [133, 71, 1.0], [134, 71, 1.0], [135, 71, 1.0], [136, 71, 1.0], [137, 71, 1.0], ...]
2	[[144, 96, 1.0], [145, 96, 1.0], [150, 96, 1.0], [151, 96, 1.0], [142, 97, 1.0], [143, 97, 1.0], [144, 97, 1.0], [145, 97, 1.0], [146, 97, 1.0], [147, 97, 1.0], [148, 97, 1.0], [149, 97, 1.0], [150, 97, 1.0], [151, 97, 1.0], [152, 97, 1.0], [140, 98, 1.0], [141, 98, 1.0], [142, 98, 1.0], [143, 98, 1.0], [144, 98, 1.0], [145, 98, 1.0], [146, 98, 1.0], [147, 98, 1.0], [148, 98, 1.0], [149, 98, 1.0], [150, 98, 1.0], [151, 98, 1.0], [152, 98, 1.0], [153, 98, 1.0], [139, 99, 1.0], [140, 99, 1.0], [141, 99, 1.0], [142, 99, 1.0], [143, 99, 1.0], [144, 99, 1.0], [145, 99, 1.0], [146, 99, 1.0], [147, 99, 1.0], [148, 99, 1.0], [149, 99, 1.0], [150, 99, 1.0], [151, 99, 1.0], [152, 99, 1.0], [153, 99, 1.0], [139, 100, 1.0], [140, 100, 1.0], [141, 100, 1.0], [142, 100, 1.0], [143, 100, 1.0], [144, 100, 1.0], [145, 100, 1.0], [146, 100, 1.0], [147, 100, 1.0], [148, 100, 1.0], [149, 100, 1.0], [150, 100, 1.0], [151, 100, 1.0], [152, 100, 1.0], [153, 100, 1.0], [139, 101, 1.0], [140, 101, 1.0], [141, 101, 1.0], [142, 101, 1.0], [143, 101, 1.0], [144, 101, 1.0], [145, 101, 1.0], [146, 101, 1.0], [147, 101, 1.0], [148, 101, 1.0], [149, 101, 1.0], [150, 101, 1.0], [151, 101, 1.0], [152, 101, 1.0], [153, 101, 1.0], [140, 102, 1.0], [141, 102, 1.0], [142, 102, 1.0], [143, 102, 1.0], [144, 102, 1.0], [145, 102, 1.0], [146, 102, 1.0], [147, 102, 1.0], [148, 102, 1.0], [149, 102, 1.0], [150, 102, 1.0], [151, 102, 1.0], [152, 102, 1.0], [142, 103, 1.0], [143, 103, 1.0], [144, 103, 1.0], [145, 103, 1.0], [146, 103, 1.0], [147, 103, 1.0], [148, 103, 1.0], [149, 103, 1.0], [150, 103, 1.0], [151, 103, 1.0], [143, 104, 1.0], [144, 104, 1.0], [145, 104, 1.0], ...]
3	[[146, 85, 1.0], [144, 86, 1.0], [145, 86, 1.0], [146, 86, 1.0], [147, 86, 1.0], [148, 86, 1.0], [149, 86, 1.0], [143, 87, 1.0], [144, 87, 1.0], [145, 87, 1.0], [146, 87, 1.0], [147, 87, 1.0], [148, 87, 1.0], [149, 87, 1.0], [150, 87, 1.0], [142, 88, 1.0], [143, 88, 1.0], [144, 88, 1.0], [145, 88, 1.0], [146, 88, 1.0], [147, 88, 1.0], [148, 88, 1.0], [149, 88, 1.0], [150, 88, 1.0], [142, 89, 1.0], [143, 89, 1.0], [144, 89, 1.0], [145, 89, 1.0], [146, 89, 1.0], [147, 89, 1.0], [148, 89, 1.0], [149, 89, 1.0], [150, 89, 1.0], [151, 89, 1.0], [141, 90, 1.0], [142, 90, 1.0], [143, 90, 1.0], [144, 90, 1.0], [145, 90, 1.0], [146, 90, 1.0], [147, 90, 1.0], [148, 90, 1.0], [149, 90, 1.0], [150, 90, 1.0], [151, 90, 1.0], [152, 90, 1.0], [141, 91, 1.0], [142, 91, 1.0], [143, 91, 1.0], [144, 91, 1.0], [145, 91, 1.0], [146, 91, 1.0], [147, 91, 1.0], [148, 91, 1.0], [149, 91, 1.0], [150, 91, 1.0], [151, 91, 1.0], [152, 91, 1.0], [142, 92, 1.0], [143, 92, 1.0], [144, 92, 1.0], [145, 92, 1.0], [146, 92, 1.0], [147, 92, 1.0], [148, 92, 1.0], [149, 92, 1.0], [150, 92, 1.0], [151, 92, 1.0], [152, 92, 1.0], [142, 93, 1.0], [143, 93, 1.0], [144, 93, 1.0], [145, 93, 1.0], [146, 93, 1.0], [147, 93, 1.0], [148, 93, 1.0], [149, 93, 1.0], [150, 93, 1.0], [151, 93, 1.0], [152, 93, 1.0], [143, 94, 1.0], [144, 94, 1.0], [145, 94, 1.0], [146, 94, 1.0], [147, 94, 1.0], [148, 94, 1.0], [149, 94, 1.0], [150, 94, 1.0], [151, 94, 1.0], [144, 95, 1.0], [145, 95, 1.0], [146, 95, 1.0], [147, 95, 1.0], [148, 95, 1.0], [149, 95, 1.0], [150, 95, 1.0], [151, 95, 1.0], [146, 96, 1.0], [147, 96, 1.0], [148, 96, 1.0], ...]

... and 44 more rows.

fluorescence_FOV3_channel1

resolution: -1.0

comments: no comments

description: Fluorescence traces from FOV3 Channel1

conversion: 1.0

offset: 0.0

unit: n.a.

data

timestamps

timestamps_unit: seconds

interval: 1

rois

description: all ROIs

table

description: Output from segmenting FOV3 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[335, 89, 1.0], [332, 90, 1.0], [333, 90, 1.0], [334, 90, 1.0], [335, 90, 1.0], [336, 90, 1.0], [337, 90, 1.0], [338, 90, 1.0], [339, 90, 1.0], [327, 91, 1.0], [328, 91, 1.0], [329, 91, 1.0], [330, 91, 1.0], [331, 91, 1.0], [332, 91, 1.0], [333, 91, 1.0], [334, 91, 1.0], [335, 91, 1.0], [336, 91, 1.0], [337, 91, 1.0], [338, 91, 1.0], [339, 91, 1.0], [340, 91, 1.0], [326, 92, 1.0], [327, 92, 1.0], [328, 92, 1.0], [329, 92, 1.0], [330, 92, 1.0], [331, 92, 1.0], [332, 92, 1.0], [333, 92, 1.0], [334, 92, 1.0], [335, 92, 1.0], [336, 92, 1.0], [337, 92, 1.0], [338, 92, 1.0], [339, 92, 1.0], [340, 92, 1.0], [341, 92, 1.0], [324, 93, 1.0], [325, 93, 1.0], [326, 93, 1.0], [327, 93, 1.0], [328, 93, 1.0], [329, 93, 1.0], [330, 93, 1.0], [331, 93, 1.0], [332, 93, 1.0], [333, 93, 1.0], [334, 93, 1.0], [335, 93, 1.0], [336, 93, 1.0], [337, 93, 1.0], [338, 93, 1.0], [339, 93, 1.0], [340, 93, 1.0], [341, 93, 1.0], [342, 93, 1.0], [323, 94, 1.0], [324, 94, 1.0], [325, 94, 1.0], [326, 94, 1.0], [327, 94, 1.0], [328, 94, 1.0], [329, 94, 1.0], [330, 94, 1.0], [331, 94, 1.0], [332, 94, 1.0], [333, 94, 1.0], [334, 94, 1.0], [335, 94, 1.0], [336, 94, 1.0], [337, 94, 1.0], [338, 94, 1.0], [339, 94, 1.0], [340, 94, 1.0], [341, 94, 1.0], [342, 94, 1.0], [322, 95, 1.0], [323, 95, 1.0], [324, 95, 1.0], [325, 95, 1.0], [326, 95, 1.0], [327, 95, 1.0], [328, 95, 1.0], [329, 95, 1.0], [330, 95, 1.0], [331, 95, 1.0], [332, 95, 1.0], [333, 95, 1.0], [334, 95, 1.0], [335, 95, 1.0], [336, 95, 1.0], [337, 95, 1.0], [338, 95, 1.0], [339, 95, 1.0], [340, 95, 1.0], [341, 95, 1.0], [342, 95, 1.0], [343, 95, 1.0], ...]
1	[[325, 77, 1.0], [326, 77, 1.0], [327, 77, 1.0], [328, 77, 1.0], [329, 77, 1.0], [323, 78, 1.0], [324, 78, 1.0], [325, 78, 1.0], [326, 78, 1.0], [327, 78, 1.0], [328, 78, 1.0], [329, 78, 1.0], [330, 78, 1.0], [321, 79, 1.0], [322, 79, 1.0], [323, 79, 1.0], [324, 79, 1.0], [325, 79, 1.0], [326, 79, 1.0], [327, 79, 1.0], [328, 79, 1.0], [329, 79, 1.0], [330, 79, 1.0], [331, 79, 1.0], [320, 80, 1.0], [321, 80, 1.0], [322, 80, 1.0], [323, 80, 1.0], [324, 80, 1.0], [325, 80, 1.0], [326, 80, 1.0], [327, 80, 1.0], [328, 80, 1.0], [329, 80, 1.0], [330, 80, 1.0], [331, 80, 1.0], [332, 80, 1.0], [319, 81, 1.0], [320, 81, 1.0], [321, 81, 1.0], [322, 81, 1.0], [323, 81, 1.0], [324, 81, 1.0], [325, 81, 1.0], [326, 81, 1.0], [327, 81, 1.0], [328, 81, 1.0], [329, 81, 1.0], [330, 81, 1.0], [331, 81, 1.0], [332, 81, 1.0], [319, 82, 1.0], [320, 82, 1.0], [321, 82, 1.0], [322, 82, 1.0], [323, 82, 1.0], [324, 82, 1.0], [325, 82, 1.0], [326, 82, 1.0], [327, 82, 1.0], [328, 82, 1.0], [329, 82, 1.0], [330, 82, 1.0], [331, 82, 1.0], [332, 82, 1.0], [333, 82, 1.0], [318, 83, 1.0], [319, 83, 1.0], [320, 83, 1.0], [321, 83, 1.0], [322, 83, 1.0], [323, 83, 1.0], [324, 83, 1.0], [325, 83, 1.0], [326, 83, 1.0], [327, 83, 1.0], [328, 83, 1.0], [329, 83, 1.0], [330, 83, 1.0], [331, 83, 1.0], [332, 83, 1.0], [333, 83, 1.0], [334, 83, 1.0], [318, 84, 1.0], [319, 84, 1.0], [320, 84, 1.0], [321, 84, 1.0], [322, 84, 1.0], [323, 84, 1.0], [324, 84, 1.0], [325, 84, 1.0], [326, 84, 1.0], [327, 84, 1.0], [328, 84, 1.0], [329, 84, 1.0], [330, 84, 1.0], [331, 84, 1.0], [332, 84, 1.0], [333, 84, 1.0], [334, 84, 1.0], ...]
2	[[351, 92, 1.0], [352, 92, 1.0], [353, 92, 1.0], [354, 92, 1.0], [355, 92, 1.0], [349, 93, 1.0], [350, 93, 1.0], [351, 93, 1.0], [352, 93, 1.0], [353, 93, 1.0], [354, 93, 1.0], [355, 93, 1.0], [356, 93, 1.0], [357, 93, 1.0], [347, 94, 1.0], [348, 94, 1.0], [349, 94, 1.0], [350, 94, 1.0], [351, 94, 1.0], [352, 94, 1.0], [353, 94, 1.0], [354, 94, 1.0], [355, 94, 1.0], [356, 94, 1.0], [357, 94, 1.0], [358, 94, 1.0], [347, 95, 1.0], [348, 95, 1.0], [349, 95, 1.0], [350, 95, 1.0], [351, 95, 1.0], [352, 95, 1.0], [353, 95, 1.0], [354, 95, 1.0], [355, 95, 1.0], [356, 95, 1.0], [357, 95, 1.0], [358, 95, 1.0], [359, 95, 1.0], [346, 96, 1.0], [347, 96, 1.0], [348, 96, 1.0], [349, 96, 1.0], [350, 96, 1.0], [351, 96, 1.0], [352, 96, 1.0], [353, 96, 1.0], [354, 96, 1.0], [355, 96, 1.0], [356, 96, 1.0], [357, 96, 1.0], [358, 96, 1.0], [359, 96, 1.0], [360, 96, 1.0], [345, 97, 1.0], [346, 97, 1.0], [347, 97, 1.0], [348, 97, 1.0], [349, 97, 1.0], [350, 97, 1.0], [351, 97, 1.0], [352, 97, 1.0], [353, 97, 1.0], [354, 97, 1.0], [355, 97, 1.0], [356, 97, 1.0], [357, 97, 1.0], [358, 97, 1.0], [359, 97, 1.0], [360, 97, 1.0], [361, 97, 1.0], [344, 98, 1.0], [345, 98, 1.0], [346, 98, 1.0], [347, 98, 1.0], [348, 98, 1.0], [349, 98, 1.0], [350, 98, 1.0], [351, 98, 1.0], [352, 98, 1.0], [353, 98, 1.0], [354, 98, 1.0], [355, 98, 1.0], [356, 98, 1.0], [357, 98, 1.0], [358, 98, 1.0], [359, 98, 1.0], [360, 98, 1.0], [361, 98, 1.0], [362, 98, 1.0], [344, 99, 1.0], [345, 99, 1.0], [346, 99, 1.0], [347, 99, 1.0], [348, 99, 1.0], [349, 99, 1.0], [350, 99, 1.0], [351, 99, 1.0], [352, 99, 1.0], [353, 99, 1.0], ...]
3	[[296, 90, 1.0], [297, 90, 1.0], [293, 91, 1.0], [294, 91, 1.0], [295, 91, 1.0], [296, 91, 1.0], [297, 91, 1.0], [298, 91, 1.0], [299, 91, 1.0], [300, 91, 1.0], [291, 92, 1.0], [292, 92, 1.0], [293, 92, 1.0], [294, 92, 1.0], [295, 92, 1.0], [296, 92, 1.0], [297, 92, 1.0], [298, 92, 1.0], [299, 92, 1.0], [300, 92, 1.0], [301, 92, 1.0], [289, 93, 1.0], [290, 93, 1.0], [291, 93, 1.0], [292, 93, 1.0], [293, 93, 1.0], [294, 93, 1.0], [295, 93, 1.0], [296, 93, 1.0], [297, 93, 1.0], [298, 93, 1.0], [299, 93, 1.0], [300, 93, 1.0], [301, 93, 1.0], [302, 93, 1.0], [288, 94, 1.0], [289, 94, 1.0], [290, 94, 1.0], [291, 94, 1.0], [292, 94, 1.0], [293, 94, 1.0], [294, 94, 1.0], [295, 94, 1.0], [296, 94, 1.0], [297, 94, 1.0], [298, 94, 1.0], [299, 94, 1.0], [300, 94, 1.0], [301, 94, 1.0], [302, 94, 1.0], [287, 95, 1.0], [288, 95, 1.0], [289, 95, 1.0], [290, 95, 1.0], [291, 95, 1.0], [292, 95, 1.0], [293, 95, 1.0], [294, 95, 1.0], [295, 95, 1.0], [296, 95, 1.0], [297, 95, 1.0], [298, 95, 1.0], [299, 95, 1.0], [300, 95, 1.0], [301, 95, 1.0], [302, 95, 1.0], [303, 95, 1.0], [286, 96, 1.0], [287, 96, 1.0], [288, 96, 1.0], [289, 96, 1.0], [290, 96, 1.0], [291, 96, 1.0], [292, 96, 1.0], [293, 96, 1.0], [294, 96, 1.0], [295, 96, 1.0], [296, 96, 1.0], [297, 96, 1.0], [298, 96, 1.0], [299, 96, 1.0], [300, 96, 1.0], [301, 96, 1.0], [302, 96, 1.0], [303, 96, 1.0], [286, 97, 1.0], [287, 97, 1.0], [288, 97, 1.0], [289, 97, 1.0], [290, 97, 1.0], [291, 97, 1.0], [292, 97, 1.0], [293, 97, 1.0], [294, 97, 1.0], [295, 97, 1.0], [296, 97, 1.0], [297, 97, 1.0], [298, 97, 1.0], [299, 97, 1.0], [300, 97, 1.0], ...]

... and 4 more rows.

image_segmentation

plane_segmentations

plane_segmentation_FOV1_channel1

description: Output from segmenting FOV1 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[150, 83, 1.0], [151, 83, 1.0], [152, 83, 1.0], [153, 83, 1.0], [154, 83, 1.0], [155, 83, 1.0], [156, 83, 1.0], [157, 83, 1.0], [158, 83, 1.0], [159, 83, 1.0], [160, 83, 1.0], [161, 83, 1.0], [149, 84, 1.0], [150, 84, 1.0], [151, 84, 1.0], [152, 84, 1.0], [153, 84, 1.0], [154, 84, 1.0], [155, 84, 1.0], [156, 84, 1.0], [157, 84, 1.0], [158, 84, 1.0], [159, 84, 1.0], [160, 84, 1.0], [161, 84, 1.0], [162, 84, 1.0], [163, 84, 1.0], [147, 85, 1.0], [148, 85, 1.0], [149, 85, 1.0], [150, 85, 1.0], [151, 85, 1.0], [152, 85, 1.0], [153, 85, 1.0], [154, 85, 1.0], [155, 85, 1.0], [156, 85, 1.0], [157, 85, 1.0], [158, 85, 1.0], [159, 85, 1.0], [160, 85, 1.0], [161, 85, 1.0], [162, 85, 1.0], [163, 85, 1.0], [164, 85, 1.0], [147, 86, 1.0], [148, 86, 1.0], [149, 86, 1.0], [150, 86, 1.0], [151, 86, 1.0], [152, 86, 1.0], [153, 86, 1.0], [154, 86, 1.0], [155, 86, 1.0], [156, 86, 1.0], [157, 86, 1.0], [158, 86, 1.0], [159, 86, 1.0], [160, 86, 1.0], [161, 86, 1.0], [162, 86, 1.0], [163, 86, 1.0], [164, 86, 1.0], [146, 87, 1.0], [147, 87, 1.0], [148, 87, 1.0], [149, 87, 1.0], [150, 87, 1.0], [151, 87, 1.0], [152, 87, 1.0], [153, 87, 1.0], [154, 87, 1.0], [155, 87, 1.0], [156, 87, 1.0], [157, 87, 1.0], [158, 87, 1.0], [159, 87, 1.0], [160, 87, 1.0], [161, 87, 1.0], [162, 87, 1.0], [163, 87, 1.0], [164, 87, 1.0], [147, 88, 1.0], [148, 88, 1.0], [149, 88, 1.0], [150, 88, 1.0], [151, 88, 1.0], [152, 88, 1.0], [153, 88, 1.0], [154, 88, 1.0], [155, 88, 1.0], [156, 88, 1.0], [157, 88, 1.0], [158, 88, 1.0], [159, 88, 1.0], [160, 88, 1.0], [161, 88, 1.0], [162, 88, 1.0], [163, 88, 1.0], [148, 89, 1.0], ...]
1	[[152, 91, 1.0], [153, 91, 1.0], [154, 91, 1.0], [155, 91, 1.0], [156, 91, 1.0], [150, 92, 1.0], [151, 92, 1.0], [152, 92, 1.0], [153, 92, 1.0], [154, 92, 1.0], [155, 92, 1.0], [156, 92, 1.0], [157, 92, 1.0], [149, 93, 1.0], [150, 93, 1.0], [151, 93, 1.0], [152, 93, 1.0], [153, 93, 1.0], [154, 93, 1.0], [155, 93, 1.0], [156, 93, 1.0], [157, 93, 1.0], [158, 93, 1.0], [148, 94, 1.0], [149, 94, 1.0], [150, 94, 1.0], [151, 94, 1.0], [152, 94, 1.0], [153, 94, 1.0], [154, 94, 1.0], [155, 94, 1.0], [156, 94, 1.0], [157, 94, 1.0], [158, 94, 1.0], [159, 94, 1.0], [148, 95, 1.0], [149, 95, 1.0], [150, 95, 1.0], [151, 95, 1.0], [152, 95, 1.0], [153, 95, 1.0], [154, 95, 1.0], [155, 95, 1.0], [156, 95, 1.0], [157, 95, 1.0], [158, 95, 1.0], [159, 95, 1.0], [160, 95, 1.0], [148, 96, 1.0], [149, 96, 1.0], [150, 96, 1.0], [151, 96, 1.0], [152, 96, 1.0], [153, 96, 1.0], [154, 96, 1.0], [155, 96, 1.0], [156, 96, 1.0], [157, 96, 1.0], [158, 96, 1.0], [159, 96, 1.0], [160, 96, 1.0], [147, 97, 1.0], [148, 97, 1.0], [149, 97, 1.0], [150, 97, 1.0], [151, 97, 1.0], [152, 97, 1.0], [153, 97, 1.0], [154, 97, 1.0], [155, 97, 1.0], [156, 97, 1.0], [157, 97, 1.0], [158, 97, 1.0], [159, 97, 1.0], [160, 97, 1.0], [161, 97, 1.0], [147, 98, 1.0], [148, 98, 1.0], [149, 98, 1.0], [150, 98, 1.0], [151, 98, 1.0], [152, 98, 1.0], [153, 98, 1.0], [154, 98, 1.0], [155, 98, 1.0], [156, 98, 1.0], [157, 98, 1.0], [158, 98, 1.0], [159, 98, 1.0], [160, 98, 1.0], [161, 98, 1.0], [148, 99, 1.0], [149, 99, 1.0], [150, 99, 1.0], [151, 99, 1.0], [152, 99, 1.0], [153, 99, 1.0], [154, 99, 1.0], [155, 99, 1.0], [156, 99, 1.0], ...]
2	[[145, 100, 1.0], [146, 100, 1.0], [147, 100, 1.0], [143, 101, 1.0], [144, 101, 1.0], [145, 101, 1.0], [146, 101, 1.0], [147, 101, 1.0], [148, 101, 1.0], [149, 101, 1.0], [142, 102, 1.0], [143, 102, 1.0], [144, 102, 1.0], [145, 102, 1.0], [146, 102, 1.0], [147, 102, 1.0], [148, 102, 1.0], [149, 102, 1.0], [150, 102, 1.0], [141, 103, 1.0], [142, 103, 1.0], [143, 103, 1.0], [144, 103, 1.0], [145, 103, 1.0], [146, 103, 1.0], [147, 103, 1.0], [148, 103, 1.0], [149, 103, 1.0], [150, 103, 1.0], [151, 103, 1.0], [140, 104, 1.0], [141, 104, 1.0], [142, 104, 1.0], [143, 104, 1.0], [144, 104, 1.0], [145, 104, 1.0], [146, 104, 1.0], [147, 104, 1.0], [148, 104, 1.0], [149, 104, 1.0], [150, 104, 1.0], [151, 104, 1.0], [152, 104, 1.0], [140, 105, 1.0], [141, 105, 1.0], [142, 105, 1.0], [143, 105, 1.0], [144, 105, 1.0], [145, 105, 1.0], [146, 105, 1.0], [147, 105, 1.0], [148, 105, 1.0], [149, 105, 1.0], [150, 105, 1.0], [151, 105, 1.0], [152, 105, 1.0], [139, 106, 1.0], [140, 106, 1.0], [141, 106, 1.0], [142, 106, 1.0], [143, 106, 1.0], [144, 106, 1.0], [145, 106, 1.0], [146, 106, 1.0], [147, 106, 1.0], [148, 106, 1.0], [149, 106, 1.0], [150, 106, 1.0], [151, 106, 1.0], [152, 106, 1.0], [139, 107, 1.0], [140, 107, 1.0], [141, 107, 1.0], [142, 107, 1.0], [143, 107, 1.0], [144, 107, 1.0], [145, 107, 1.0], [146, 107, 1.0], [147, 107, 1.0], [148, 107, 1.0], [149, 107, 1.0], [150, 107, 1.0], [151, 107, 1.0], [152, 107, 1.0], [139, 108, 1.0], [140, 108, 1.0], [141, 108, 1.0], [142, 108, 1.0], [143, 108, 1.0], [144, 108, 1.0], [145, 108, 1.0], [146, 108, 1.0], [147, 108, 1.0], [148, 108, 1.0], [149, 108, 1.0], [150, 108, 1.0], [151, 108, 1.0], [152, 108, 1.0], [140, 109, 1.0], [141, 109, 1.0], ...]
3	[[138, 90, 1.0], [139, 90, 1.0], [140, 90, 1.0], [137, 91, 1.0], [138, 91, 1.0], [139, 91, 1.0], [140, 91, 1.0], [141, 91, 1.0], [136, 92, 1.0], [137, 92, 1.0], [138, 92, 1.0], [139, 92, 1.0], [140, 92, 1.0], [141, 92, 1.0], [142, 92, 1.0], [143, 92, 1.0], [135, 93, 1.0], [136, 93, 1.0], [137, 93, 1.0], [138, 93, 1.0], [139, 93, 1.0], [140, 93, 1.0], [141, 93, 1.0], [142, 93, 1.0], [143, 93, 1.0], [144, 93, 1.0], [134, 94, 1.0], [135, 94, 1.0], [136, 94, 1.0], [137, 94, 1.0], [138, 94, 1.0], [139, 94, 1.0], [140, 94, 1.0], [141, 94, 1.0], [142, 94, 1.0], [143, 94, 1.0], [144, 94, 1.0], [134, 95, 1.0], [135, 95, 1.0], [136, 95, 1.0], [137, 95, 1.0], [138, 95, 1.0], [139, 95, 1.0], [140, 95, 1.0], [141, 95, 1.0], [142, 95, 1.0], [143, 95, 1.0], [144, 95, 1.0], [145, 95, 1.0], [134, 96, 1.0], [135, 96, 1.0], [136, 96, 1.0], [137, 96, 1.0], [138, 96, 1.0], [139, 96, 1.0], [140, 96, 1.0], [141, 96, 1.0], [142, 96, 1.0], [143, 96, 1.0], [144, 96, 1.0], [135, 97, 1.0], [136, 97, 1.0], [137, 97, 1.0], [138, 97, 1.0], [139, 97, 1.0], [140, 97, 1.0], [141, 97, 1.0], [142, 97, 1.0], [143, 97, 1.0], [144, 97, 1.0], [135, 98, 1.0], [136, 98, 1.0], [137, 98, 1.0], [138, 98, 1.0], [139, 98, 1.0], [140, 98, 1.0], [141, 98, 1.0], [142, 98, 1.0], [143, 98, 1.0], [144, 98, 1.0], [136, 99, 1.0], [137, 99, 1.0], [138, 99, 1.0], [139, 99, 1.0], [140, 99, 1.0], [141, 99, 1.0], [142, 99, 1.0], [143, 99, 1.0], [137, 100, 1.0], [138, 100, 1.0], [139, 100, 1.0], [140, 100, 1.0], [141, 100, 1.0], [142, 100, 1.0], [138, 101, 1.0], [139, 101, 1.0], [140, 101, 1.0]]

... and 13 more rows.

plane_segmentation_FOV2_channel1

description: Output from segmenting FOV2 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[159, 51, 1.0], [160, 51, 1.0], [154, 52, 1.0], [155, 52, 1.0], [156, 52, 1.0], [157, 52, 1.0], [158, 52, 1.0], [159, 52, 1.0], [160, 52, 1.0], [161, 52, 1.0], [162, 52, 1.0], [151, 53, 1.0], [152, 53, 1.0], [153, 53, 1.0], [154, 53, 1.0], [155, 53, 1.0], [156, 53, 1.0], [157, 53, 1.0], [158, 53, 1.0], [159, 53, 1.0], [160, 53, 1.0], [161, 53, 1.0], [162, 53, 1.0], [163, 53, 1.0], [150, 54, 1.0], [151, 54, 1.0], [152, 54, 1.0], [153, 54, 1.0], [154, 54, 1.0], [155, 54, 1.0], [156, 54, 1.0], [157, 54, 1.0], [158, 54, 1.0], [159, 54, 1.0], [160, 54, 1.0], [161, 54, 1.0], [162, 54, 1.0], [163, 54, 1.0], [149, 55, 1.0], [150, 55, 1.0], [151, 55, 1.0], [152, 55, 1.0], [153, 55, 1.0], [154, 55, 1.0], [155, 55, 1.0], [156, 55, 1.0], [157, 55, 1.0], [158, 55, 1.0], [159, 55, 1.0], [160, 55, 1.0], [161, 55, 1.0], [162, 55, 1.0], [163, 55, 1.0], [164, 55, 1.0], [149, 56, 1.0], [150, 56, 1.0], [151, 56, 1.0], [152, 56, 1.0], [153, 56, 1.0], [154, 56, 1.0], [155, 56, 1.0], [156, 56, 1.0], [157, 56, 1.0], [158, 56, 1.0], [159, 56, 1.0], [160, 56, 1.0], [161, 56, 1.0], [162, 56, 1.0], [163, 56, 1.0], [164, 56, 1.0], [148, 57, 1.0], [149, 57, 1.0], [150, 57, 1.0], [151, 57, 1.0], [152, 57, 1.0], [153, 57, 1.0], [154, 57, 1.0], [155, 57, 1.0], [156, 57, 1.0], [157, 57, 1.0], [158, 57, 1.0], [159, 57, 1.0], [160, 57, 1.0], [161, 57, 1.0], [162, 57, 1.0], [163, 57, 1.0], [164, 57, 1.0], [165, 57, 1.0], [148, 58, 1.0], [149, 58, 1.0], [150, 58, 1.0], [151, 58, 1.0], [152, 58, 1.0], [153, 58, 1.0], [154, 58, 1.0], [155, 58, 1.0], [156, 58, 1.0], [157, 58, 1.0], [158, 58, 1.0], [159, 58, 1.0], ...]
1	[[136, 63, 1.0], [137, 63, 1.0], [138, 63, 1.0], [139, 63, 1.0], [140, 63, 1.0], [141, 63, 1.0], [135, 64, 1.0], [136, 64, 1.0], [137, 64, 1.0], [138, 64, 1.0], [139, 64, 1.0], [140, 64, 1.0], [141, 64, 1.0], [142, 64, 1.0], [143, 64, 1.0], [134, 65, 1.0], [135, 65, 1.0], [136, 65, 1.0], [137, 65, 1.0], [138, 65, 1.0], [139, 65, 1.0], [140, 65, 1.0], [141, 65, 1.0], [142, 65, 1.0], [143, 65, 1.0], [133, 66, 1.0], [134, 66, 1.0], [135, 66, 1.0], [136, 66, 1.0], [137, 66, 1.0], [138, 66, 1.0], [139, 66, 1.0], [140, 66, 1.0], [141, 66, 1.0], [142, 66, 1.0], [143, 66, 1.0], [144, 66, 1.0], [133, 67, 1.0], [134, 67, 1.0], [135, 67, 1.0], [136, 67, 1.0], [137, 67, 1.0], [138, 67, 1.0], [139, 67, 1.0], [140, 67, 1.0], [141, 67, 1.0], [142, 67, 1.0], [143, 67, 1.0], [144, 67, 1.0], [145, 67, 1.0], [133, 68, 1.0], [134, 68, 1.0], [135, 68, 1.0], [136, 68, 1.0], [137, 68, 1.0], [138, 68, 1.0], [139, 68, 1.0], [140, 68, 1.0], [141, 68, 1.0], [142, 68, 1.0], [143, 68, 1.0], [144, 68, 1.0], [145, 68, 1.0], [132, 69, 1.0], [133, 69, 1.0], [134, 69, 1.0], [135, 69, 1.0], [136, 69, 1.0], [137, 69, 1.0], [138, 69, 1.0], [139, 69, 1.0], [140, 69, 1.0], [141, 69, 1.0], [142, 69, 1.0], [143, 69, 1.0], [144, 69, 1.0], [145, 69, 1.0], [146, 69, 1.0], [132, 70, 1.0], [133, 70, 1.0], [134, 70, 1.0], [135, 70, 1.0], [136, 70, 1.0], [137, 70, 1.0], [138, 70, 1.0], [139, 70, 1.0], [140, 70, 1.0], [141, 70, 1.0], [142, 70, 1.0], [143, 70, 1.0], [144, 70, 1.0], [145, 70, 1.0], [146, 70, 1.0], [147, 70, 1.0], [132, 71, 1.0], [133, 71, 1.0], [134, 71, 1.0], [135, 71, 1.0], [136, 71, 1.0], [137, 71, 1.0], ...]
2	[[144, 96, 1.0], [145, 96, 1.0], [150, 96, 1.0], [151, 96, 1.0], [142, 97, 1.0], [143, 97, 1.0], [144, 97, 1.0], [145, 97, 1.0], [146, 97, 1.0], [147, 97, 1.0], [148, 97, 1.0], [149, 97, 1.0], [150, 97, 1.0], [151, 97, 1.0], [152, 97, 1.0], [140, 98, 1.0], [141, 98, 1.0], [142, 98, 1.0], [143, 98, 1.0], [144, 98, 1.0], [145, 98, 1.0], [146, 98, 1.0], [147, 98, 1.0], [148, 98, 1.0], [149, 98, 1.0], [150, 98, 1.0], [151, 98, 1.0], [152, 98, 1.0], [153, 98, 1.0], [139, 99, 1.0], [140, 99, 1.0], [141, 99, 1.0], [142, 99, 1.0], [143, 99, 1.0], [144, 99, 1.0], [145, 99, 1.0], [146, 99, 1.0], [147, 99, 1.0], [148, 99, 1.0], [149, 99, 1.0], [150, 99, 1.0], [151, 99, 1.0], [152, 99, 1.0], [153, 99, 1.0], [139, 100, 1.0], [140, 100, 1.0], [141, 100, 1.0], [142, 100, 1.0], [143, 100, 1.0], [144, 100, 1.0], [145, 100, 1.0], [146, 100, 1.0], [147, 100, 1.0], [148, 100, 1.0], [149, 100, 1.0], [150, 100, 1.0], [151, 100, 1.0], [152, 100, 1.0], [153, 100, 1.0], [139, 101, 1.0], [140, 101, 1.0], [141, 101, 1.0], [142, 101, 1.0], [143, 101, 1.0], [144, 101, 1.0], [145, 101, 1.0], [146, 101, 1.0], [147, 101, 1.0], [148, 101, 1.0], [149, 101, 1.0], [150, 101, 1.0], [151, 101, 1.0], [152, 101, 1.0], [153, 101, 1.0], [140, 102, 1.0], [141, 102, 1.0], [142, 102, 1.0], [143, 102, 1.0], [144, 102, 1.0], [145, 102, 1.0], [146, 102, 1.0], [147, 102, 1.0], [148, 102, 1.0], [149, 102, 1.0], [150, 102, 1.0], [151, 102, 1.0], [152, 102, 1.0], [142, 103, 1.0], [143, 103, 1.0], [144, 103, 1.0], [145, 103, 1.0], [146, 103, 1.0], [147, 103, 1.0], [148, 103, 1.0], [149, 103, 1.0], [150, 103, 1.0], [151, 103, 1.0], [143, 104, 1.0], [144, 104, 1.0], [145, 104, 1.0], ...]
3	[[146, 85, 1.0], [144, 86, 1.0], [145, 86, 1.0], [146, 86, 1.0], [147, 86, 1.0], [148, 86, 1.0], [149, 86, 1.0], [143, 87, 1.0], [144, 87, 1.0], [145, 87, 1.0], [146, 87, 1.0], [147, 87, 1.0], [148, 87, 1.0], [149, 87, 1.0], [150, 87, 1.0], [142, 88, 1.0], [143, 88, 1.0], [144, 88, 1.0], [145, 88, 1.0], [146, 88, 1.0], [147, 88, 1.0], [148, 88, 1.0], [149, 88, 1.0], [150, 88, 1.0], [142, 89, 1.0], [143, 89, 1.0], [144, 89, 1.0], [145, 89, 1.0], [146, 89, 1.0], [147, 89, 1.0], [148, 89, 1.0], [149, 89, 1.0], [150, 89, 1.0], [151, 89, 1.0], [141, 90, 1.0], [142, 90, 1.0], [143, 90, 1.0], [144, 90, 1.0], [145, 90, 1.0], [146, 90, 1.0], [147, 90, 1.0], [148, 90, 1.0], [149, 90, 1.0], [150, 90, 1.0], [151, 90, 1.0], [152, 90, 1.0], [141, 91, 1.0], [142, 91, 1.0], [143, 91, 1.0], [144, 91, 1.0], [145, 91, 1.0], [146, 91, 1.0], [147, 91, 1.0], [148, 91, 1.0], [149, 91, 1.0], [150, 91, 1.0], [151, 91, 1.0], [152, 91, 1.0], [142, 92, 1.0], [143, 92, 1.0], [144, 92, 1.0], [145, 92, 1.0], [146, 92, 1.0], [147, 92, 1.0], [148, 92, 1.0], [149, 92, 1.0], [150, 92, 1.0], [151, 92, 1.0], [152, 92, 1.0], [142, 93, 1.0], [143, 93, 1.0], [144, 93, 1.0], [145, 93, 1.0], [146, 93, 1.0], [147, 93, 1.0], [148, 93, 1.0], [149, 93, 1.0], [150, 93, 1.0], [151, 93, 1.0], [152, 93, 1.0], [143, 94, 1.0], [144, 94, 1.0], [145, 94, 1.0], [146, 94, 1.0], [147, 94, 1.0], [148, 94, 1.0], [149, 94, 1.0], [150, 94, 1.0], [151, 94, 1.0], [144, 95, 1.0], [145, 95, 1.0], [146, 95, 1.0], [147, 95, 1.0], [148, 95, 1.0], [149, 95, 1.0], [150, 95, 1.0], [151, 95, 1.0], [146, 96, 1.0], [147, 96, 1.0], [148, 96, 1.0], ...]

... and 44 more rows.

plane_segmentation_FOV3_channel1

description: Output from segmenting FOV3 Channel 1.

imaging_plane

optical_channel

0

description: Green emitted light was collected through a 525/50 filter and a gallium arsenide phosphide photomultiplier tube (Hamamatsu).

emission_lambda: 525.0

description: Imaging plane for the Green channel recorded with 2p microscope.

device

description: Janelia 2P-RAM mesoscope.

manufacturer: ThorLabs

excitation_lambda: 920.0

indicator: GCaMP6f

location: OB

conversion: 1.0

unit: meters

origin_coords_unit: meters

grid_spacing_unit: meters

table

	pixel_mask
id
0	[[335, 89, 1.0], [332, 90, 1.0], [333, 90, 1.0], [334, 90, 1.0], [335, 90, 1.0], [336, 90, 1.0], [337, 90, 1.0], [338, 90, 1.0], [339, 90, 1.0], [327, 91, 1.0], [328, 91, 1.0], [329, 91, 1.0], [330, 91, 1.0], [331, 91, 1.0], [332, 91, 1.0], [333, 91, 1.0], [334, 91, 1.0], [335, 91, 1.0], [336, 91, 1.0], [337, 91, 1.0], [338, 91, 1.0], [339, 91, 1.0], [340, 91, 1.0], [326, 92, 1.0], [327, 92, 1.0], [328, 92, 1.0], [329, 92, 1.0], [330, 92, 1.0], [331, 92, 1.0], [332, 92, 1.0], [333, 92, 1.0], [334, 92, 1.0], [335, 92, 1.0], [336, 92, 1.0], [337, 92, 1.0], [338, 92, 1.0], [339, 92, 1.0], [340, 92, 1.0], [341, 92, 1.0], [324, 93, 1.0], [325, 93, 1.0], [326, 93, 1.0], [327, 93, 1.0], [328, 93, 1.0], [329, 93, 1.0], [330, 93, 1.0], [331, 93, 1.0], [332, 93, 1.0], [333, 93, 1.0], [334, 93, 1.0], [335, 93, 1.0], [336, 93, 1.0], [337, 93, 1.0], [338, 93, 1.0], [339, 93, 1.0], [340, 93, 1.0], [341, 93, 1.0], [342, 93, 1.0], [323, 94, 1.0], [324, 94, 1.0], [325, 94, 1.0], [326, 94, 1.0], [327, 94, 1.0], [328, 94, 1.0], [329, 94, 1.0], [330, 94, 1.0], [331, 94, 1.0], [332, 94, 1.0], [333, 94, 1.0], [334, 94, 1.0], [335, 94, 1.0], [336, 94, 1.0], [337, 94, 1.0], [338, 94, 1.0], [339, 94, 1.0], [340, 94, 1.0], [341, 94, 1.0], [342, 94, 1.0], [322, 95, 1.0], [323, 95, 1.0], [324, 95, 1.0], [325, 95, 1.0], [326, 95, 1.0], [327, 95, 1.0], [328, 95, 1.0], [329, 95, 1.0], [330, 95, 1.0], [331, 95, 1.0], [332, 95, 1.0], [333, 95, 1.0], [334, 95, 1.0], [335, 95, 1.0], [336, 95, 1.0], [337, 95, 1.0], [338, 95, 1.0], [339, 95, 1.0], [340, 95, 1.0], [341, 95, 1.0], [342, 95, 1.0], [343, 95, 1.0], ...]
1	[[325, 77, 1.0], [326, 77, 1.0], [327, 77, 1.0], [328, 77, 1.0], [329, 77, 1.0], [323, 78, 1.0], [324, 78, 1.0], [325, 78, 1.0], [326, 78, 1.0], [327, 78, 1.0], [328, 78, 1.0], [329, 78, 1.0], [330, 78, 1.0], [321, 79, 1.0], [322, 79, 1.0], [323, 79, 1.0], [324, 79, 1.0], [325, 79, 1.0], [326, 79, 1.0], [327, 79, 1.0], [328, 79, 1.0], [329, 79, 1.0], [330, 79, 1.0], [331, 79, 1.0], [320, 80, 1.0], [321, 80, 1.0], [322, 80, 1.0], [323, 80, 1.0], [324, 80, 1.0], [325, 80, 1.0], [326, 80, 1.0], [327, 80, 1.0], [328, 80, 1.0], [329, 80, 1.0], [330, 80, 1.0], [331, 80, 1.0], [332, 80, 1.0], [319, 81, 1.0], [320, 81, 1.0], [321, 81, 1.0], [322, 81, 1.0], [323, 81, 1.0], [324, 81, 1.0], [325, 81, 1.0], [326, 81, 1.0], [327, 81, 1.0], [328, 81, 1.0], [329, 81, 1.0], [330, 81, 1.0], [331, 81, 1.0], [332, 81, 1.0], [319, 82, 1.0], [320, 82, 1.0], [321, 82, 1.0], [322, 82, 1.0], [323, 82, 1.0], [324, 82, 1.0], [325, 82, 1.0], [326, 82, 1.0], [327, 82, 1.0], [328, 82, 1.0], [329, 82, 1.0], [330, 82, 1.0], [331, 82, 1.0], [332, 82, 1.0], [333, 82, 1.0], [318, 83, 1.0], [319, 83, 1.0], [320, 83, 1.0], [321, 83, 1.0], [322, 83, 1.0], [323, 83, 1.0], [324, 83, 1.0], [325, 83, 1.0], [326, 83, 1.0], [327, 83, 1.0], [328, 83, 1.0], [329, 83, 1.0], [330, 83, 1.0], [331, 83, 1.0], [332, 83, 1.0], [333, 83, 1.0], [334, 83, 1.0], [318, 84, 1.0], [319, 84, 1.0], [320, 84, 1.0], [321, 84, 1.0], [322, 84, 1.0], [323, 84, 1.0], [324, 84, 1.0], [325, 84, 1.0], [326, 84, 1.0], [327, 84, 1.0], [328, 84, 1.0], [329, 84, 1.0], [330, 84, 1.0], [331, 84, 1.0], [332, 84, 1.0], [333, 84, 1.0], [334, 84, 1.0], ...]
2	[[351, 92, 1.0], [352, 92, 1.0], [353, 92, 1.0], [354, 92, 1.0], [355, 92, 1.0], [349, 93, 1.0], [350, 93, 1.0], [351, 93, 1.0], [352, 93, 1.0], [353, 93, 1.0], [354, 93, 1.0], [355, 93, 1.0], [356, 93, 1.0], [357, 93, 1.0], [347, 94, 1.0], [348, 94, 1.0], [349, 94, 1.0], [350, 94, 1.0], [351, 94, 1.0], [352, 94, 1.0], [353, 94, 1.0], [354, 94, 1.0], [355, 94, 1.0], [356, 94, 1.0], [357, 94, 1.0], [358, 94, 1.0], [347, 95, 1.0], [348, 95, 1.0], [349, 95, 1.0], [350, 95, 1.0], [351, 95, 1.0], [352, 95, 1.0], [353, 95, 1.0], [354, 95, 1.0], [355, 95, 1.0], [356, 95, 1.0], [357, 95, 1.0], [358, 95, 1.0], [359, 95, 1.0], [346, 96, 1.0], [347, 96, 1.0], [348, 96, 1.0], [349, 96, 1.0], [350, 96, 1.0], [351, 96, 1.0], [352, 96, 1.0], [353, 96, 1.0], [354, 96, 1.0], [355, 96, 1.0], [356, 96, 1.0], [357, 96, 1.0], [358, 96, 1.0], [359, 96, 1.0], [360, 96, 1.0], [345, 97, 1.0], [346, 97, 1.0], [347, 97, 1.0], [348, 97, 1.0], [349, 97, 1.0], [350, 97, 1.0], [351, 97, 1.0], [352, 97, 1.0], [353, 97, 1.0], [354, 97, 1.0], [355, 97, 1.0], [356, 97, 1.0], [357, 97, 1.0], [358, 97, 1.0], [359, 97, 1.0], [360, 97, 1.0], [361, 97, 1.0], [344, 98, 1.0], [345, 98, 1.0], [346, 98, 1.0], [347, 98, 1.0], [348, 98, 1.0], [349, 98, 1.0], [350, 98, 1.0], [351, 98, 1.0], [352, 98, 1.0], [353, 98, 1.0], [354, 98, 1.0], [355, 98, 1.0], [356, 98, 1.0], [357, 98, 1.0], [358, 98, 1.0], [359, 98, 1.0], [360, 98, 1.0], [361, 98, 1.0], [362, 98, 1.0], [344, 99, 1.0], [345, 99, 1.0], [346, 99, 1.0], [347, 99, 1.0], [348, 99, 1.0], [349, 99, 1.0], [350, 99, 1.0], [351, 99, 1.0], [352, 99, 1.0], [353, 99, 1.0], ...]
3	[[296, 90, 1.0], [297, 90, 1.0], [293, 91, 1.0], [294, 91, 1.0], [295, 91, 1.0], [296, 91, 1.0], [297, 91, 1.0], [298, 91, 1.0], [299, 91, 1.0], [300, 91, 1.0], [291, 92, 1.0], [292, 92, 1.0], [293, 92, 1.0], [294, 92, 1.0], [295, 92, 1.0], [296, 92, 1.0], [297, 92, 1.0], [298, 92, 1.0], [299, 92, 1.0], [300, 92, 1.0], [301, 92, 1.0], [289, 93, 1.0], [290, 93, 1.0], [291, 93, 1.0], [292, 93, 1.0], [293, 93, 1.0], [294, 93, 1.0], [295, 93, 1.0], [296, 93, 1.0], [297, 93, 1.0], [298, 93, 1.0], [299, 93, 1.0], [300, 93, 1.0], [301, 93, 1.0], [302, 93, 1.0], [288, 94, 1.0], [289, 94, 1.0], [290, 94, 1.0], [291, 94, 1.0], [292, 94, 1.0], [293, 94, 1.0], [294, 94, 1.0], [295, 94, 1.0], [296, 94, 1.0], [297, 94, 1.0], [298, 94, 1.0], [299, 94, 1.0], [300, 94, 1.0], [301, 94, 1.0], [302, 94, 1.0], [287, 95, 1.0], [288, 95, 1.0], [289, 95, 1.0], [290, 95, 1.0], [291, 95, 1.0], [292, 95, 1.0], [293, 95, 1.0], [294, 95, 1.0], [295, 95, 1.0], [296, 95, 1.0], [297, 95, 1.0], [298, 95, 1.0], [299, 95, 1.0], [300, 95, 1.0], [301, 95, 1.0], [302, 95, 1.0], [303, 95, 1.0], [286, 96, 1.0], [287, 96, 1.0], [288, 96, 1.0], [289, 96, 1.0], [290, 96, 1.0], [291, 96, 1.0], [292, 96, 1.0], [293, 96, 1.0], [294, 96, 1.0], [295, 96, 1.0], [296, 96, 1.0], [297, 96, 1.0], [298, 96, 1.0], [299, 96, 1.0], [300, 96, 1.0], [301, 96, 1.0], [302, 96, 1.0], [303, 96, 1.0], [286, 97, 1.0], [287, 97, 1.0], [288, 97, 1.0], [289, 97, 1.0], [290, 97, 1.0], [291, 97, 1.0], [292, 97, 1.0], [293, 97, 1.0], [294, 97, 1.0], [295, 97, 1.0], [296, 97, 1.0], [297, 97, 1.0], [298, 97, 1.0], [299, 97, 1.0], [300, 97, 1.0], ...]

... and 4 more rows.

Plot Correlation Images¶

In [14]:

correlation_images_names = list(nwbfile.processing["ophys"]["correlation_images"].images.keys())
_ = [print(name) for name in correlation_images_names]

correlation_images_names = list(nwbfile.processing["ophys"]["correlation_images"].images.keys()) _ = [print(name) for name in correlation_images_names]

correlation_image_FOV1_channel1
correlation_image_FOV2_channel1
correlation_image_FOV3_channel1

In [15]:

fig, axs = plt.subplots(nrows=len(correlation_images_names), ncols=1, sharex=True, sharey=True)
for ci_index,ci_name in enumerate(correlation_images_names):    
    correlation_image = nwbfile.processing["ophys"]["correlation_images"].images[ci_name]
    axs[ci_index].imshow(correlation_image.data[:].T, cmap="binary")
    axs[ci_index].set_title(ci_name.replace("_"," "))
plt.show()

fig, axs = plt.subplots(nrows=len(correlation_images_names), ncols=1, sharex=True, sharey=True) for ci_index,ci_name in enumerate(correlation_images_names): correlation_image = nwbfile.processing["ophys"]["correlation_images"].images[ci_name] axs[ci_index].imshow(correlation_image.data[:].T, cmap="binary") axs[ci_index].set_title(ci_name.replace("_"," ")) plt.show()

Plot Average Images¶

In [16]:

average_images_names = list(nwbfile.processing["ophys"]["average_images"].images.keys())
_ = [print(name) for name in average_images_names]

average_images_names = list(nwbfile.processing["ophys"]["average_images"].images.keys()) _ = [print(name) for name in average_images_names]

average_image_FOV1_channel1
average_image_FOV2_channel1
average_image_FOV3_channel1

In [17]:

fig, axs = plt.subplots(nrows=len(average_images_names), ncols=1, sharex=True, sharey=True)
for ai_index,ai_name in enumerate(average_images_names):    
    average_image = nwbfile.processing["ophys"]["average_images"].images[ai_name]
    axs[ai_index].imshow(average_image.data[:].T, cmap="binary")
    axs[ai_index].set_title(ai_name.replace("_"," "))
plt.show()

fig, axs = plt.subplots(nrows=len(average_images_names), ncols=1, sharex=True, sharey=True) for ai_index,ai_name in enumerate(average_images_names): average_image = nwbfile.processing["ophys"]["average_images"].images[ai_name] axs[ai_index].imshow(average_image.data[:].T, cmap="binary") axs[ai_index].set_title(ai_name.replace("_"," ")) plt.show()

Plot Image Segmentation¶

In [18]:

plane_segmentation_names = list(nwbfile.processing["ophys"]["image_segmentation"].plane_segmentations.keys())
_ = [print(name) for name in nwbfile.processing["ophys"]["image_segmentation"].plane_segmentations.keys()]

plane_segmentation_names = list(nwbfile.processing["ophys"]["image_segmentation"].plane_segmentations.keys()) _ = [print(name) for name in nwbfile.processing["ophys"]["image_segmentation"].plane_segmentations.keys()]

plane_segmentation_FOV1_channel1
plane_segmentation_FOV2_channel1
plane_segmentation_FOV3_channel1

In [19]:

ROI_index = 0

fig, axs = plt.subplots(nrows=len(plane_segmentation_names), ncols=1, sharex=True, sharey=True)
for ps_index,ps_name in enumerate(plane_segmentation_names):    
    average_image = nwbfile.processing["ophys"]["average_images"].images[average_images_names[ps_index]]
    axs[ps_index].imshow(average_image.data[:].T, cmap="binary")
    plane_segmentation = nwbfile.processing["ophys"]["image_segmentation"].plane_segmentations[ps_name]
    x_coordinates = [pix[0] for pix in plane_segmentation[ROI_index].pixel_mask[0]]
    y_coordinates = [pix[1] for pix in plane_segmentation[ROI_index].pixel_mask[0]]
    axs[ps_index].scatter(x_coordinates, y_coordinates, color="green")
    axs[ps_index].set_title(ps_name.replace("_"," "))
plt.show()

ROI_index = 0 fig, axs = plt.subplots(nrows=len(plane_segmentation_names), ncols=1, sharex=True, sharey=True) for ps_index,ps_name in enumerate(plane_segmentation_names): average_image = nwbfile.processing["ophys"]["average_images"].images[average_images_names[ps_index]] axs[ps_index].imshow(average_image.data[:].T, cmap="binary") plane_segmentation = nwbfile.processing["ophys"]["image_segmentation"].plane_segmentations[ps_name] x_coordinates = [pix[0] for pix in plane_segmentation[ROI_index].pixel_mask[0]] y_coordinates = [pix[1] for pix in plane_segmentation[ROI_index].pixel_mask[0]] axs[ps_index].scatter(x_coordinates, y_coordinates, color="green") axs[ps_index].set_title(ps_name.replace("_"," ")) plt.show()

Visualize fluorescence traces¶

The fluroscence traces are stored in a Fluorescence container, the raw traces can be accessed as nwbfile.processing["ophys"]["Fluorescence"]["RoiResponseSeries"].

In [20]:

import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)

fluorescence_traces_names = [name for name in nwbfile.processing["ophys"]["fluorescence"].roi_response_series.keys()]

fluorescence_traces_name = fluorescence_traces_names[0]
fluorescence_traces = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].data[:1000, :10]
df = pd.DataFrame(fluorescence_traces)
df["time"] = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].timestamps[:1000]
df.set_index("time", inplace=True)
df.columns.name = 'ROIs'

import plotly.express as px

fig = px.line(df, facet_row="ROIs", facet_row_spacing=0.01)

# hide and lock down axes
fig.update_xaxes(visible=True, fixedrange=False)
fig.update_yaxes(visible=False, fixedrange=False)

# remove facet/subplot labels
fig.update_layout(annotations=[], overwrite=True)

# strip down the rest of the plot
fig.update_layout(
    showlegend=True,
    plot_bgcolor="white",
    margin=dict(t=10, l=10, b=10, r=10)
)

fig.show(config=dict(displayModeBar=True))

import pandas as pd import warnings warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) fluorescence_traces_names = [name for name in nwbfile.processing["ophys"]["fluorescence"].roi_response_series.keys()] fluorescence_traces_name = fluorescence_traces_names[0] fluorescence_traces = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].data[:1000, :10] df = pd.DataFrame(fluorescence_traces) df["time"] = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].timestamps[:1000] df.set_index("time", inplace=True) df.columns.name = 'ROIs' import plotly.express as px fig = px.line(df, facet_row="ROIs", facet_row_spacing=0.01) # hide and lock down axes fig.update_xaxes(visible=True, fixedrange=False) fig.update_yaxes(visible=False, fixedrange=False) # remove facet/subplot labels fig.update_layout(annotations=[], overwrite=True) # strip down the rest of the plot fig.update_layout( showlegend=True, plot_bgcolor="white", margin=dict(t=10, l=10, b=10, r=10) ) fig.show(config=dict(displayModeBar=True))

Access Odor stimuli ¶

This section demonstrates how to access the odor stimulation data and metadata.

In [21]:

nwbfile.trials.to_dataframe()

nwbfile.trials.to_dataframe()

Out[21]:

	start_time	stop_time	odorant	concentration	solution_date
id
0	59.2888	60.2928	Methyl Salicylate	0.1	2022-05-17
1	60.2966	61.2995	Methyl Salicylate	0.1	2022-05-17
2	61.3034	62.3067	Methyl Salicylate	0.1	2022-05-17
3	62.3106	63.3130	Methyl Salicylate	0.1	2022-05-17
4	63.3170	64.3213	Methyl Salicylate	0.1	2022-05-17
...	...	...	...	...	...
495	9675.0000	9676.0000	Methyl Salicylate	0.1	2022-05-17
496	9676.0000	9677.0100	Methyl Salicylate	0.1	2022-05-17
497	9677.0100	9678.0100	Methyl Salicylate	0.1	2022-05-17
498	9678.0200	9679.0200	Methyl Salicylate	0.1	2022-05-17
499	9679.0200	9680.0300	Methyl Salicylate	0.1	2022-05-17

500 rows × 5 columns

In [22]:

# Prepare data for plotting
import numpy as np
ROI_index=0
start=0
stop=-1
fluorescence_data = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].data[start:stop, ROI_index]
fluorescence_timestamps = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].timestamps[start:stop]

fig, ax = plt.subplots(figsize=(6, 2), dpi=300)
line_handle, = ax.plot(fluorescence_timestamps, fluorescence_data, color="green", linewidth=0.5, label="ROI {ROI_index}".format(ROI_index=ROI_index))

stimulus_table = nwbfile.trials.to_dataframe()
methyl_salicylate_stim = "Methyl Salicylate"
methyl_salicylate_stim_table = stimulus_table[stimulus_table["odorant"]==methyl_salicylate_stim]
methyl_salicylate_stim_handles = []
for (stimulus_start, stimulus_stop) in zip(methyl_salicylate_stim_table["start_time"], methyl_salicylate_stim_table["stop_time"]):
    methyl_salicylate_stim_handle = ax.fill_between(fluorescence_timestamps, np.nanmax(fluorescence_data), where=(fluorescence_timestamps >= stimulus_start) & (fluorescence_timestamps <= stimulus_stop),
                                    color='yellow', edgecolor='none', alpha=0.9, label=methyl_salicylate_stim)
    methyl_salicylate_stim_handles.append(methyl_salicylate_stim_handle)

allyl_sulfide_stim = "Allyl Sulfide"
allyl_sulfide_stim_table = stimulus_table[stimulus_table["odorant"]==allyl_sulfide_stim]
allyl_sulfide_stim_handles = []
for (stimulus_start, stimulus_stop) in zip(allyl_sulfide_stim_table["start_time"], allyl_sulfide_stim_table["stop_time"]):
    allyl_sulfide_stim_handle = ax.fill_between(fluorescence_timestamps, np.nanmax(fluorescence_data), where=(fluorescence_timestamps >= stimulus_start) & (fluorescence_timestamps <= stimulus_stop),
                                    color='blue', edgecolor='none', alpha=0.9, label=allyl_sulfide_stim)
    allyl_sulfide_stim_handles.append(allyl_sulfide_stim_handle)

# Hide top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Create the legend with the appropriate handles
handles = [line_handle, methyl_salicylate_stim_handles[0], allyl_sulfide_stim_handles[0]]
labels = ["ROI {ROI_index}".format(ROI_index=ROI_index), methyl_salicylate_stim, allyl_sulfide_stim]
ax.legend(handles, labels, frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})

ax.tick_params(axis='y', labelsize=8)
plt.xlabel('Time (s)', fontsize=8)
plt.tick_params(axis='x', labelsize=8)
plt.show()

# Prepare data for plotting import numpy as np ROI_index=0 start=0 stop=-1 fluorescence_data = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].data[start:stop, ROI_index] fluorescence_timestamps = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].timestamps[start:stop] fig, ax = plt.subplots(figsize=(6, 2), dpi=300) line_handle, = ax.plot(fluorescence_timestamps, fluorescence_data, color="green", linewidth=0.5, label="ROI {ROI_index}".format(ROI_index=ROI_index)) stimulus_table = nwbfile.trials.to_dataframe() methyl_salicylate_stim = "Methyl Salicylate" methyl_salicylate_stim_table = stimulus_table[stimulus_table["odorant"]==methyl_salicylate_stim] methyl_salicylate_stim_handles = [] for (stimulus_start, stimulus_stop) in zip(methyl_salicylate_stim_table["start_time"], methyl_salicylate_stim_table["stop_time"]): methyl_salicylate_stim_handle = ax.fill_between(fluorescence_timestamps, np.nanmax(fluorescence_data), where=(fluorescence_timestamps >= stimulus_start) & (fluorescence_timestamps <= stimulus_stop), color='yellow', edgecolor='none', alpha=0.9, label=methyl_salicylate_stim) methyl_salicylate_stim_handles.append(methyl_salicylate_stim_handle) allyl_sulfide_stim = "Allyl Sulfide" allyl_sulfide_stim_table = stimulus_table[stimulus_table["odorant"]==allyl_sulfide_stim] allyl_sulfide_stim_handles = [] for (stimulus_start, stimulus_stop) in zip(allyl_sulfide_stim_table["start_time"], allyl_sulfide_stim_table["stop_time"]): allyl_sulfide_stim_handle = ax.fill_between(fluorescence_timestamps, np.nanmax(fluorescence_data), where=(fluorescence_timestamps >= stimulus_start) & (fluorescence_timestamps <= stimulus_stop), color='blue', edgecolor='none', alpha=0.9, label=allyl_sulfide_stim) allyl_sulfide_stim_handles.append(allyl_sulfide_stim_handle) # Hide top and right spines ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) # Create the legend with the appropriate handles handles = [line_handle, methyl_salicylate_stim_handles[0], allyl_sulfide_stim_handles[0]] labels = ["ROI {ROI_index}".format(ROI_index=ROI_index), methyl_salicylate_stim, allyl_sulfide_stim] ax.legend(handles, labels, frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8}) ax.tick_params(axis='y', labelsize=8) plt.xlabel('Time (s)', fontsize=8) plt.tick_params(axis='x', labelsize=8) plt.show()

Access Treadmill Traces ¶

This section demonstrates how to access the treadmill data.

In [23]:

nwbfile.acquisition["treadmill_position"]

nwbfile.acquisition["treadmill_position"]

Out[23]:

treadmill_position (SpatialSeries)

resolution: -1.0

comments: no comments

description: treadmill position from Treadmill table

conversion: 1.0

offset: 0.0

unit: meters

data

timestamps

timestamps_unit: seconds

interval: 1

reference_frame: unknown

timestamp_link

0: processing/behavior/treadmill_velocity/timestamps

In [24]:

start=0
stop=-1
treadmill_data = nwbfile.acquisition["treadmill_position"].data[start:stop]
treadmill_timestamps = nwbfile.acquisition["treadmill_position"].timestamps[start:stop]

fig, ax = plt.subplots(figsize=(6, 2), dpi=300)
ax.plot(treadmill_timestamps, treadmill_data, color="red", linewidth=0.5, label="treadmill position")

# Hide top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Create the 
ax.legend(frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})

ax.tick_params(axis='y', labelsize=8)
plt.xlabel('Time (s)', fontsize=8)
plt.tick_params(axis='x', labelsize=8)
plt.show()

start=0 stop=-1 treadmill_data = nwbfile.acquisition["treadmill_position"].data[start:stop] treadmill_timestamps = nwbfile.acquisition["treadmill_position"].timestamps[start:stop] fig, ax = plt.subplots(figsize=(6, 2), dpi=300) ax.plot(treadmill_timestamps, treadmill_data, color="red", linewidth=0.5, label="treadmill position") # Hide top and right spines ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) # Create the ax.legend(frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8}) ax.tick_params(axis='y', labelsize=8) plt.xlabel('Time (s)', fontsize=8) plt.tick_params(axis='x', labelsize=8) plt.show()

In [25]:

nwbfile.processing["behavior"]["treadmill_velocity"]

nwbfile.processing["behavior"]["treadmill_velocity"]

Out[25]:

treadmill_velocity (TimeSeries)

resolution: -1.0

comments: no comments

description: treadmill velocity from Treadmill table

conversion: 1.0

offset: 0.0

unit: unknown

data

timestamps (link to acquisition/treadmill_position/timestamps)

timestamps_unit: seconds

interval: 1

In [26]:

data = nwbfile.processing["behavior"]["treadmill_velocity"].data[start:stop]
treadmill_timestamps = nwbfile.acquisition["treadmill_position"].timestamps[start:stop]

fig, ax = plt.subplots(figsize=(6, 2), dpi=300)
ax.plot(fluorescence_timestamps, fluorescence_data, linewidth=0.5, label="fluorescence trace")
ax.plot(treadmill_timestamps, data*25, linewidth=0.5, label="treadmill velocity")

# Hide top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Create the 
ax.legend(frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})

ax.tick_params(axis='y', labelsize=8)
plt.xlabel('Time (s)', fontsize=8)
plt.tick_params(axis='x', labelsize=8)
plt.show()

data = nwbfile.processing["behavior"]["treadmill_velocity"].data[start:stop] treadmill_timestamps = nwbfile.acquisition["treadmill_position"].timestamps[start:stop] fig, ax = plt.subplots(figsize=(6, 2), dpi=300) ax.plot(fluorescence_timestamps, fluorescence_data, linewidth=0.5, label="fluorescence trace") ax.plot(treadmill_timestamps, data*25, linewidth=0.5, label="treadmill velocity") # Hide top and right spines ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) # Create the ax.legend(frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8}) ax.tick_params(axis='y', labelsize=8) plt.xlabel('Time (s)', fontsize=8) plt.tick_params(axis='x', labelsize=8) plt.show()

In [ ]: